Month: April 2018

Introduction: Data Politics Beyond Liberation and Protection?

“Data” has become an important keyword in contemporary life. It features prominently in many different visions of the future, as well as in relation to a wide range of practical tasks. Companies see data as a lucrative new asset class and as a resource for streamlining their operations and for providing new offerings. Politicians see data as an instrument of reform by enabling transparency, accountability, participation and innovation. Journalists see data as a means to source stories and enrich their reportage. Activists see data as both an issue in itself and as a resource for intervention concerning everything from corporate and governmental surveillance to climate change and migration. Data is envisaged to make money, strengthen democracies, aid investigations and enable justice. At the same time it has been subjected to numerous critiques. Data is also held to disrupt livelihoods, violate privacy, undermine democracies, deepen inequalities, distract from issues, and displace other forms of reasoning, sense-making and experience.

What are we to make of what appears concurrently as an almost magical object of attention and concern, as well an integral part of the mundane organisation of daily affairs? Data has become an object of study in numerous fields, and has even given rise to new fields and sub-fields such as “data studies” and “critical data studies” (see, e.g. Kitchin and Lauriault 2014; Iliadis and Russo 2016; Dalton, Taylor and Thatcher 2016). This article introduces the notion of “data worlds” and explores its relevance for studying, theorising and doing things with data. It draws on previous research on worlds, worlding and world-making in order to examine three aspects of data worlds as: (i) horizons of intelligibility, (ii) collective accomplishments, and (iii) transnational coordination. These three aspects are illustrated with examples from ongoing research on the politics of public data. The article concludes by reflecting on how the notion of data worlds might inform not only social and cultural research, but also inspire interventions and experimentation around the politics of data.

While using the concept of data worlds in my own research, I’ve been reviewing how others use it. The term has been mentioned in relation to topics such as “big and small data worlds” (Blok and Pedersen 2014), “data art” (Singer 2016), “test bed urbanism” (Halpern, LeCavalier, Calvillo and Pietsch 2013) and “thing ethnography” (Giaccardi, Cila, Speed and Caldwell 2016). Many of these brief references do not dwell on what is meant by the term. This article addresses the gap in this literature by unpacking the notion of data worlds and suggesting three closely related ways in which it can be understood. Before looking at these in more depth I will briefly say a bit more about why and how the concept of data worlds may be useful in relation to studying the politics of data, and why I focus on the particular set of ideas about worlds, worlding and world-making that informs the discussion below.

The notion of data worlds is used in my work in order to look beyond data as a “representational resource”, to consider the various forms of epistemic, social and political work that it does and which is done to produce it. The representational conception is evident in both implicit metaphors and explicit models for talking about and doing things with data. While data does indeed designate aspects of situations, it could also do other things, such as shape the way we see and think about things, serve as a common point of connection across situations, and help to conventionalise ways of organising the world.

The notion of data worlds is thus partly a response to contemporary “socio-technical imaginaries” (Jasanoff and Kim 2015) about data. Just as industrial technologies of the past were accompanied by new social, cultural and political imaginaries, so we can trace the ascent of “data imaginaries” and “data speak”: visions and rhetoric concerning the role of data in society. As Gillespie notes in relation to platforms, these imaginaries do “discursive work” (2010). For example, data is framed as “the new oil”, “the new gold” or “the new soil”, in order to emphasise its value as a social or economic resource. We also see the idea of “infrastructures” of data being used in order to emphasise different configurations of public-private and state-citizen collaboration, as well as to establish information infrastructures as a basic good in society alongside infrastructures for water, gas, electricity and so on. The platform, the portal, the app, the lab and the hackday give rise to new imaginaries and discursive regimes as well as material practices suggesting the role of data in public life.

Many of these imaginaries focus on the value to be extracted from data, through various mechanisms and arrangements to make data public. The issue is often framed as one of access, formats and conventions for encouraging the re-use of public data in innovative applications and services. I have found the notion of data worlds to be useful in examining what open data initiatives do and do not do, and how they might be done differently (Gray 2018). For example, open data projects may focus on redistributing access to data about public finances without substantively engaging with the epistemic, social and political work of data infrastructures in selecting, translating, arranging and articulating certain aspects of fiscal policy (such as detailed spending estimates of local councils), but not others (such as the economic activities and tax payments of multinational corporations).

The notion of data worlds is intended to gesture beyond two prominent forms of data politics which can be broadly characterised as “data liberation” and “data protection”. Both emphasise dynamics of power related to access. Data liberation is widely associated with hacker culture and other forms of information activism: setting information free from institutions and corporations as a means to address information asymmetries, and to provide a resource for activism and social change. This may be considered in terms of a “Promethean” mythology of broadening access to a powerful resource or instrument (just as Prometheus stole fire from the gods to give to humankind) – an outlook which is shared across the spectrum from the “mega-leaks” of Wikileaks and the Panama Papers and the more curated, selective leaks of Edward Snowden, through to “Freedom of Information” (FOI) and access to information movements in the 1990s, as well as official and grassroots open data initiatives which emphasise making data legally and technically amenable to re-use (Gray 2016).

On the flipside, we have “data protection” as a narrative of information politics which emphasises the protection of personal information from state, corporate and other actors – as is exemplified in the work of civil society groups such as Privacy International and the Electronic Frontier Foundation. These narratives place an emphasis on the individual ownership and control of personal data, as well as on preventing, obstructing, managing, regulating and raising awareness of the collection of personally identifying information – from artistic projects to make visible the personal information different actors have collected, to law and policy (such as EU Data Protection rules or US Fair Information Principles).

Whilst these two genres of information policy and information politics are indeed vital, data infrastructures do much more than making data public and making data private. Raymond Geuss has critiqued what he considers the disproportionate attention accorded to the “public/private distinction” which both reflects and reinforces the absence of “any effective general framework for thinking about politics apart from liberalism” (Geuss 2003). Dominic Boyer has suggested the phrase “digital liberalism” as an invitation to attend to how “techno-institutional processes such as computerization and digital information and politico-institutional discourses of late liberalism have coevolved, at times reinforcing and naturalizing each other, promoting novel bundles of epistemics and ethics” (Boyer 2013).

The notion of data worlds is intended to make space for thinking about data as more than simply a representational resource, and the politics of data as more than a matter of liberation and protection. It is intended to encourage exploration of the performative capacities of data infrastructures: what they do and could do differently, and how they are done and could be done differently. This includes consideration of, as Geoffrey Bowker puts it, “the ways in which our social, cultural and political values are braided into the wires, coded into the applications and built into the databases which are so much a part of our daily lives” (2014). In doing so we may draw on performative analyses of numbers (Espeland and Stevens 2008; Verran 2015), models (Mackenzie 2008) and methods (Law, Ruppert and Savage 2011) to consider how data infrastructures may be involved in not just the representation but also the articulation of collective life, while at the same time being the products of social and institutional work themselves.

Many accounts of performativity allude to the work of J. L. Austin, who suggests “the issuing of an utterance is the performing of an action” (1975: 6). Austin is associated with a “linguistic turn” in Anglophone analytic philosophy said to begin with Wittgenstein, whose later work reflects on what language does beyond referring to things. In the following discussion of the performative and world-making capacities of data infrastructures, I draw on an earlier linguistic turn that occurred in German philosophy in the eighteenth century and which has recently begun to receive more attention in English-language scholarship (Lifschitz 2012; Bowie 2013; Taylor 2016). Thinkers associated with this earlier turn also sought to look beyond representational accounts of language towards its other capacities as a situated set of social practices. Ian Hacking argues this tradition can be viewed as an alternative to Wittgenstein’s “depoliticized” philosophy of language (2002). I do not argue for the special relevance of this period and these ideas. Rather I suggest that it contains conceptual and theoretical resources which may be useful when considering different aspects of worlds, worlding and world-making in relation to data.

The three aspects of data worlds which I examine below are not intended to be comprehensive, but illustrative of what is involved in data infrastructures, what they do, and how they are put to work. As I shall return to in the conclusion, this outline is intended to open up space for not only thinking about data differently, but also doing things with data differently. The test of these three aspects is therefore not only their analytical purchase, but also their practical utility.

1. Data Worlds as Horizons of Intelligibility

The first aspect of data worlds draws on philosophical ideas about worlds, worlding and world-making to look at how things are sayable, knowable, intelligible and experiencable through data. In European philosophy this begins with Kant’s “Copernican revolution” which recognises the active and creative role that human beings played in composing the worlds that they experience – including through schemes, categories and structures such as space, time, causality and quantity which give form to experience. This is an explicit departure from views which saw experience as “given” and immediate, and also heralds a broader philosophical shift towards looking at how experience is articulated and mediated through language, culture and social arrangements.

Subsequent thinkers in this tradition –Hamann and Herder in the eighteenth century to thinkers as diverse as Heidegger, Gadamer, Benjamin and Wittgenstein in the twentieth century – stripped Kant’s project of its aspiration to clarify universal structures, and highlighted the role of socially and historically situated linguistic and cultural infrastructures, or what the contemporary philosopher Charles Taylor calls “meaningful media” (Taylor 1985), in shaping our apprehension of the worlds we inhabit.

Many of these earlier thinkers mainly focused on the role of language as a horizon of intelligibility, providing the “conditions of possibility” for our experience. As Taylor notes, this also corresponded with an explicit move away from a dominant focus on the designative, representational and “information encoding” capacities of language and other meaningful media – and a focus on their role in composing and co-producing our worlds of experience (2016). As Hacking puts it, in this tradition we find the notion that: “language is creative; to it we owe the existences and structures that populate our world-versions” (2002, 139). And yet, while there is a focus on language as an important and paradigmatic case of how our experience is formed, language is very often construed in a broad sense – including not only written and verbal language, but also music, painting, sculpture, and other social and cultural conventions for making meaning.

Benjamin draws on Hamann’s “metacritical” challenge towards narrower conceptions of experience as “naked, primitive, self-evident” (Benjamin 1996), exploring in his work the world-making capacities of architecture, urban planning, fashion, advertising and technologies, perhaps most famously in his Arcades Project (Benjamin 1999). Later in the twentieth century, these kinds of appropriations of Kantian ideas about schematism and world-making (minus the transcendental idealist baggage), have broadened out from what Apel calls the “linguistic a priori” of thinkers like Hamann, Herder and Heidegger (Apel 1973, 39), to the “historical a priori” of Michel Foucault (Foucault 1972) and what has been called the “technological a priori” of German media theorists shifting the focus to Kulturtechniken or “cultural techniques” (Tuschling 2016; Winthrop-Young, Iurascu and Parikka 2013).

What might this sense of world-making bring to an understanding of the politics of data? Taking a cue from this theoretical constellation, we might envisage data worlding in terms of a contingent, historically and socially situated, technologically mediated “data a priori” which not only designates but also provides the conditions of possibility for seeing and engaging with different aspects of collective life – making possible particular styles of reasoning and particular forms of knowledge and experience.

Data practices might be understood not just in terms of more sophisticated ways of picking things out, but as contributing to new ways of making things up, as Hacking puts it (1985). Here critical data scholars can benefit from decades of research on social practices of quantification (Porter 1996; Espeland and Stevens 2008; Rottenburg, Merry, Park and Mugler 2015; Bruno, Jany-Catrice and Touchelay 2016); statistics (Porter 1986; Desrosières 2002); standards (Lampland and Star 2008); probability (Hacking 1990); visual reasoning (Halpern 2015); and other studies of cultures and practices of knowledge which focus not just on what is said, but on the background against which things become sayable. In looking at how data worlds provide horizons of intelligibility we can both draw on genealogies of the modes of experience and styles of reasoning which are rendered possible through data over previous decades and centuries, as well as looking at what is distinctive about new and emerging digital technologies. As Nelson Goodman puts it in his classic Ways of Worldmaking: “worldmaking as we know it always starts from worlds already on hand; the making is a remaking” (Goodman 1978, 6).

Figure 1: Screenshot of transportation maps from Mapnificent project (mapnificent.net), showing which places are accessible in a given amount of time from a given point.

Thus in relation to digital data worlds we may examine how composites of conventions, norms, technologies, practices, methods, pieces of software, graphical user interfaces, data standards, data formats and aesthetic approaches are implicated in making things up and making things intelligible with data. This might include looking at how horizons of intelligibility change from pre-digital to digital data worlds. For example, we might look at differences in how the world is conceptually organised or “carved up” into categories. In contrast to the classificatory practices of statisticians taking measure of economies or populations, “born digital” and big data, generated as a result of interactions with online platforms, can give rise to novel practices of semi-automated classification, as well as emerging forms of inequality and discrimination.

Figure 2: Screenshot of interactive “animated bubble charts” of Gapminder project (gapminder.org), exploring relations between average life expectancy and income per capita over time for countries around the world.

There are many historical studies looking at how social categories are articulated through statistical practices (Desrosières 2002). In digital data worlds computational techniques such as machine learning may be used to facilitate “class discovery”. Clusters and orderings of hashtags, links, likes, images and other media can be viewed as co-produced by the logic of platforms, algorithms, and the “device cultures” of users. For example, Sam Lavigne’s “Taxonomy of Humans According to Twitter” at The New Inquiry explores and visually represents the “bizarre rubrics Twitter uses to render its users legible” (Lavigne 2017). This project aims to make visible the way in which people are classified according to a combination of user activity and information from data-brokerage companies, leading to categories such as “people who live with three other people”, “buyers of frozen ethnic foods”, and “households whose behavior indicates they are spa mavens”. These algorithmically-mediated data practices around online platforms can be understood, as Annemarie Mol puts it, as “new ways of doing reality” (Mol 1999).

We might also look at the forms of experience, styles of reasoning, and genres of sociality that arise with novel kinds of cultural objects associated with digital data worlds. This includes the world-making capacities of things such as apps, platforms, software packages, code libraries, and data analysis and visualisation tools through which people make sense with data, and integrate data into different kinds of social processes, practices and institutions. We might consider how space, time, relations and categories are articulated and organised through lists, tables, charts, timelines, maps and coordinate systems – and inscribed into dashboards, interactive data visualisations, word clouds, network graphs, mapping technologies, and computational techniques for filtering, reconciling and analysing data.

Just as Scott talks of “seeing like a state” by reducing “an infinite array of detail to a set of categories that will facilitate summary descriptions, comparisons, and aggregation” (1999, 77), and Law talks of “seeing like a survey” by using statistical methods to enact “a very particular version of the collective” (2009), so we may consider how the performative and world-making capacities of data projects are conventionalised into familiar forms such as seeing like an app, a network graph, a data portal, an API, an interactive map, a Google Spreadsheet and so on.

Figure 3: Detail of dashboard previews from London Datastore (data.london.gov.uk) showing trends in relation to performance indicators for the city.

“Time travel” maps articulate novel and interactive relationships between space and time by estimating the zones that can be reached from a given point in a given time interval (Figure 1). Global indicators are no longer simply represented through tables, charts or line graphs, but through interactive animated graphics dramatizing the passage of centuries through the movements of multicoloured bubbles articulating different dimensions of collective life (Figure 2). Interactive dashboards are envisaged as the preferred mode to increase transparency and public accountability in the city by tracking trends in relation to key performance indicators (Figure 3). While these kinds of projects draw on ideals and practices that have much longer histories – such as the aspiration for what Theodore Porter characterises as “thin descriptions” and an aesthetic of distance – digital technologies are also facilitating reconfigurations and redistributions of these data world-making capacities, leading to emerging genres of making sense with data. As we shall see in the following section, these meaning-making practices should be understood as social conventions.

2. Data Worlds as Collective Accomplishments

The second aspect of data worlds draws on a sociological tradition of studying “social worlds”. Adele Clarke and Susan Leigh Star trace this from the Chicago School of Sociology to Science and Technology Studies (Clarke and Star 2008). This approach encompasses and informs a range of research on social worlds – including for example Anselm Strauss, who suggests in the 1970s that we should look at the “social worlds” of genetics, high energy physics, computerisation and banking (Strauss 1978), to Howard Becker’s renowned work on “art worlds” (Becker 1984), as well as the “worlds of classification” and “information worlds” explored in the work of Bowker, Star and other scholars of information infrastructures (Bowker and Star 2000; Star, Bowker and Neumann 1997).

This view of social world-making is also commensurate with both critics and radical interpreters of Kant who suggest that language and meaning-making practices should be regarded in fundamentally social and historical terms – a move which led Ian Hacking to mark this as a key moment when language “goes public” (2002). This tendency to look at language and meaning-making practices in terms of contingent and evolving social institutions is also present in Wittgenstein’s work, which is a formative influence on subsequent social research agendas from ethnomethodology, to the “Strong Programme”, to Science and Technology Studies (see, e.g. Bloor 1983, 2002; Hacking 1984; Lynch 1992).

Taking a cue from this tradition, we might look at how the information products, styles of reasoning, and meaning-making capacities associated with data infrastructures can be considered as “relational achievements” or “distributed accomplishments” – and how the collectives associated with data infrastructures are changing in composition. Data worlds as horizons of intelligibility must thus be understood as social and collective. Changes in these collectives can carry significant political and political-economic consequences. For example, in the case of the redistribution of “data work” from official institutions to actors outside the public sector – as in the case of open data initiatives (Gray 2018), to civil society and citizen generated data (Gray, Lämmerhirt and Bounegru, 2016), through a shift of emphasis from statistical data to “big data” generated by major technology companies (Flyverbom, Madsen and Rasche 2017).

In Howard Becker’s terms, we can examine the “conventions” and practices which hold these social “data worlds” together – which he characterises as “ways of seeing and hearing that were known by everyone involved and thus formed the basis for their collective action” (Becker 1984, xv). In the case of open data, this might include, for example, such things as open licensing practices, legal arrangements, and technical practices which aim to “unlock the potential” of data as a resource, and “reduce the barriers” to its re-use by non-state actors – whether in new technology products such as Google Maps, the stories of data journalists, or the campaigns of NGOs or civil society groups. This concern with legal and technical conventions suggests that the open data community might be understood as what Chris Kelty calls a “recursive public”, or “a public that is vitally concerned with the material and practical maintenance and modification of the technical, legal, practical, and conceptual means of its own existence as a public” (Kelty 2005, 3). There are also emerging conventions for making sense with data such as those discussed in the previous section.

Looking at data worlds as collective accomplishments includes recognising the role of actors whose contributions may otherwise be under-recognised. In his work on the sociology of “art worlds” Becker suggests a shift of emphasis from the formal quality of art works to “complex networks through which art happens” (1984, 1). In his work he describes a broad range of materials, formats, spaces, instruments, distribution networks and art workers which are involved in the production and distribution of art works, and the assembly of their publics. Hence we might survey not just the formal properties of data projects or practices of prominent data workers (such as data scientists or data journalists), but a much broader cast of characters who are involved in the production, circulation and reception of data work.

Similar moves will be familiar from approaches inspired by Science and Technology Studies which view data infrastructures as relations of people, machines, software, standards, processes, practices, and cultures of knowledge production (e.g. Bowker and Star 1998, 2000; Star 1999; Star and Bowker 2002; Star and Ruhleder 1996; Jackson, Edwards, Bowker and Knobel 2007). Susan Leigh Star and Geoffrey Bowker suggest the notion of “infrastructural inversion” to bring neglected actors and processes into the foreground, including the role of non-human actors. In other recent work this has been framed in terms of “data assemblages” (see, e.g., Kitchin and Lauriault 2014).

One notable feature of many aspects of contemporary data politics is the emphasis on redistributing different forms of data work through digital technologies and networks. This redistribution comes in many different flavours. The tendency to redistribute “data work” from the public sector to the private sector is reflected in what Joseph Stiglitz calls the “default position” in information policy in the US, which is that states should not attempt what can be more effectively delivered by markets. This sentiment is also echoed in an influential paper called “Government Data and the Invisible Hand”, suggesting that states cannot “keep pace” with the internet. This paper is picked up by Tim O’Reilly with his idea of “government as a platform” (which he opposed to “vending machine government”), an idea which was institutionalised as part of government policy in the UK (Gray 2014). Since the turn of the millennium, public information policy has seen an influx of different ideas concerning how and why to redistribute public data work – from enabling new kinds of innovation and businesses, reducing public sector costs, to crowdsourcing, distributed collaboration or peer production around data (modelled on open source software development), to grassroots, bottom up and participatory data cultures.

The redistribution of data worlds can be facilitated through a variety of devices and conventions, such as open licenses (like Creative Commons licenses); data formats such as Google Transit Feed Specification (later renamed General Transit Feed Specification); online platforms such as GitHub; data portals (such as data.gov); as well as hackathons, fellowships, and other public engagement activities. We may consider these not only as “transparency devices” (Barry 2010), but also as “infrastructuring devices” (Star and Bowker 2002; Pipek and Wulf 2009; Björgvinsson, Ehn and Hillgren 2010; Karasti 2014; Le Dantec and DiSalvo 2013), assembling different publics around data, whether it is to clean it up, crowdsource quality control of bus stop locations, monitor potholes, or make new apps and services. How these different forms of publicity, participation and contribution are materially organised is an important question which can be read in relation to recent research on the politics of openness and participation (Tkacz 2014) and of platforms, platformisation and platform capitalism (Helmond 2015; Srnicek 2016).

3. Data Worlds as Transnational Coordination

A third aspect of data worlds is world-making as transnational coordination, which includes projects of shaping, governing and articulating transnational relations, from empires and international institution building, to the networks, circuits and tendencies which are often studied by sociologists of globalisation (Sassen 2006).

Through this lens we can look at the world-making ambitions of legal and technical norms, standardisation, harmonisation and interoperability processes undertaken by a wide variety of different actors in the service of different projects for making things global. For example, UN bodies and EU statistical agencies have undertaken extensive programmes of work to align national forms of quantification – to support transnational policy coordination and comparison. Intergovernmental actors and international organisations such as the IMF, the World Bank and the UN, have long supported the creation and alignment of systems and standards for the management of public finances in order to support objectives such as “fiscal discipline”, the allocation and coordination of development funds, and the comparability of public spending across borders.

It is not only public institutions which share these kinds of world-shaping ambitions by means of data. They are accompanied by a host of researchers, companies, statisticians, consultants, analysts, accountants, scientists, activists, technologists, managers, journalists, ecologists, librarians, and others who seek to establish transnational information systems, practices, norms and standards. This may range from professional standards bodies (such as the International Accounting Standards Board), to multinational consultancies (such as Deloitte and other “big four” accounting firms), to private technology companies or startups (big tech companies such as Google to smaller projects like OpenCorporates), to non-profit and civil society initiatives (such as the Open Contracting Partnership’s work on procurement data or Data2X’s work on gender data).

Such initiatives often aim to shape the world through the coordination of data. Data worlds can make things amenable to measurement, monitoring, evaluation, analysis, and visualisation across space and time in support of diverse political, geo-political, eco-political or political economic programmes – from neoliberal fiscal policy to market creation, gender equality to tax justice, increasing biodiversity to strengthening democracy. Civil society interventions to create and shape global data can be read in terms of other recent work around the history and sociology of quantification, as well as in terms of what some researchers have called “statactivism”, and, more recently, “data activism” (Bruno, Didier and Vitale, 2014; Milan and van der Velden 2016). Longstanding information infrastructure projects, such as Amnesty International’s “Urgent Actions” database, can be viewed as a kind of transnational “issue work”, in order to render what might otherwise be disconnected incidents amenable to classification, measurement, comparison and virtual witnessing across borders.

Figure 4. “Urgent Actions Visualised” from Amnesty International’s Decoders project. http://decoders.amnesty.org/projects/decode-urgent-actions/results

There are of course many ways in which a given issue or matter of concern may be articulated, defined, parameterised, quantified, and given life through data. The transnational coordination aspect of data world-making is multivalent and may be considered in relation to a wide variety of utopian and dystopian, progressive and regressive political and ecological projects, as well as in terms of different histories and conceptions of land, territory, empire and earth. Here we may benefit from previous research on the colonial aspects of worlding in literary and cultural studies (e.g. Spivak 1985; Karagiannis and Wagner 2007; Clark, Finlay, and Kelly 2017); post-colonial computing (e.g. Irani, Vertesi, Dourish, Philip and Grinter, 2010); “planetary-scale computation” and emerging “technogeographies” (Bratton 2016; Gabrys 2016); as well as the surge of interest around “global intellectual history” and the making of worlds (e.g. Bell 2013).

We may consider data worlds to facilitate the demarcation and shaping of spaces, territories, environments, categories, identities and boundaries, separating interior from exterior, and sorting things, people and places out. They may also direct attention to different kinds of transnational issues, dynamics, concerns or collectives. For example, we may look at the role of data worlds in relation to notions of the Anthropocene and the Capitalocene, in order to look at the role of human activity on a geological scale, as well as in the service of anthropologies of modernity, and projects to, as Bruno Latour puts it, “recompose a common world” (Kunkel 2017; Haraway 2016; Latour 2013). As well as deploying data worlds in order to better understand how human activity shapes the earth, information infrastructures may also be used to attempt to take various ecological signals into account in collectively redirecting its trajectory. As Goodman puts it: “if there is one world, it embraces a multiplicity of contrasting aspects; if there are many worlds, the collection of them all is one” (1978, 2). Data infrastructures can be used to establish the material character and limits of our one earth which contains such a plurality of social worlds and world-versions.

Data worlds can thus be understood as a means to institutionalise different forms of transnational coordination by providing the background against which things become seeable, sayable and doable with data across borders. Following recent research on neoliberal programmes (Roberts 2011; Davies 2014), data worlds may be considered as part of projects for reconfiguring relations between states, markets, citizens and civil society by foregrounding rankings, ratings and regimes of valuation in order to reinforce ideas of performance, competition and innovation, at the same time as moving tenets of economic and fiscal policy outside the realm of public and political deliberation. We can also read the redistributions of various forms of data work in terms of these contemporary imaginaries of democracy, markets and information – including those of competition, accountability, transparency, innovation, self-optimising systems and specific configurations of centralised management and decision-making coupled with decentralised delivery and contribution.

While there are indeed data worlds which may be configured to accelerate marketisation, bureaucratisation and what Habermas characterises as the “colonisation of the lifeworld”; other projects seek to address inequality and injustice, or to hold powerful elites accountable (as emphasised by the “statactivism” tradition), and all else in between. Data worlds can be malleable and may have unexpected consequences – such as in the cases of reports for investors being used by journalists and activists, or data from international development organisations being used by credit agencies.

Conclusion: Other Data Worlds Are Possible?

The aspects of data worlds described above are intended to gesture beyond two prominent narratives of data politics: of Promethean conceptions of liberating data as a resource on the one hand, and Orwellian visions of data surveillance, privacy and data protection on the other. These are vital parts of contemporary information politics, but there are other important aspects of what data is and what data does that should not be overlooked.

This article explores how theoretical traditions and literatures about worlds, worlding and world-making may be brought to bear to suggest different ways of thinking about data politics, highlighting three closely related aspects of data worlds. These three aspects are intended to be illustrative not exhaustive, and are intended as overlapping rather than distinct lenses through which to consider data infrastructures. They give rise to three different but closely related sets of questions for researching, theorising and reflecting on different aspects of data worlds.

1. Data worlds as horizons of intelligibility: What are the epistemic world-making capacities of data infrastructures? How might data infrastructures be involved in “making things up”? Can they provide conditions of possibility for different ways of seeing, saying and knowing collective life, and if so, how?
2. Data worlds as collective accomplishments: Who and what is involved with making, and making sense with, data? How are data worlds being redistributed through digital technologies? Who is (and who isn’t) able to shape data worlds? What kinds of practices of participation and public involvement are emerging around data worlds?
3. Data worlds as transnational coordination: How might data infrastructures be implicated in different attempts to “make things global”? What kinds of transnational alliances and circuits are being formed and to what end? Who advocates which kinds of data worlds, and according to which kinds of visions and fields of transnational coordination (from international relations to earth science)?

It is worth noting that it remains an empirical question as to how and to what extent data infrastructures are involved in world-making in these three senses. Data infrastructures can be deployed with certain epistemic, social and political aspirations and imaginaries in mind which they do not live up to. Data projects can fail to become data worlds in these three senses.

The notion of data worlds is not just intended to advance research on data politics. Following recent debates about the performativity of critique (e.g. Latour 2004), and calls to integrate critical, theoretical and humanistic reflection into technical practice (e.g. Agre 1997; Rieder and Röhle 2012; Berry 2014), I am particularly interested in how the notion of data worlds might suggest different kinds of data politics. Of course, theory and critique can contribute to doing things differently, as critical data studies researchers have pointed out. Dalton, Taylor and Thatcher, for example, propose to “develop alternative knowledges that reflect and build on our criticisms” (2016).

To this end, I’d like to propose the notion of “critical data practice” as a site for pedagogical experimentation, research and intervention around the politics of data. This follows Agre’s notion of “critical technical practice” which he uses to characterise his attempts to integrate historical and theoretical reflection around artificial intelligence into his work as an AI researcher (Agre 1997). The crucial question here is what difference critical studies can make in doing things with data. As well as contributing to critical genealogies and sociologies of the politics of “data worlds” and “data world-making” projects, researchers and universities might contribute to “making space” for such experimentation and intervention around public data infrastructures and the role they play in collective life.

The three aspects of data worlds I have examined are intended to assist with the task of rethinking the politics of public data, by considering how and for whom it is made public. Thus we may examine the organisation of what Evelyn Ruppert calls “data publics” (Ruppert 2015) beyond a focus on accessing, liberating and using data, and taking a broader look at how different actors engage with, mobilise around, shape and are shaped by, public data infrastructures. This includes distributed collaboration around different kinds of “data work” – from projects inspired by free software, free culture and open access movements such as Open Street Map or Wikidata, to data journalism and data activism projects for counting police killings or migrant deaths, to other kinds of civil society interventions for changing the socio-technical arrangements by which public institutions account for issues by means of data. As well as attending to these arrangements, researchers may also consider “experiments in participation” (Lezaun, Marres and Tironi 2016; Marres 2012) around data worlds, which are also cognisant of patterning and politics of these participatory processes. Such experimentation would not just aim to interpret data worlds, but also to question them, to re-imagine them, and to change them.

Acknowledgements

I’d like to thank Frank Pasquale, Will Davies, Liz McFall, Daniel Wilson and other participants at a workshop on “Outnumbered! Statistics, Data and the Public Interest” at the Centre for Research in the Arts, Social Sciences and Humanities (CRASSH), University of Cambridge in June 2017 for their useful feedback on an earlier version of this paper. It has also benefitted from feedback and input from colleagues in talks and workshops at King’s College London, the University of Amsterdam, the Politecnico di Milano, the University of Westminster, the University of Siegen and the Université Paris Nanterre. I’m also most grateful for time, suggestions and encouragement from Claudia Aradau, Liliana Bounegru, Carolin Gerlitz, Noorje Marres, John Durham Peters and two anonymous reviewers. Parts of the research for this article were made possible by a Starting Grant of the European Research Council (639379-DATACTIVE, https://data-activism.net) at the University of Amsterdam, where I was a postdoctoral researcher (2015-2016) and continue as a Research Associate; as well as through a Prize Fellowship at the Institute for Policy Research, University of Bath (2016-2017).

Introduction

It is now widely accepted that data are oiling the twenty-first century (Toonders 2014). Data gathering and tracking are practically universal, and datafication (the quantification of aspects of life previously experienced in qualitative, non-numeric form, such as communication, relationships, health and fitness, transport and mobility, democratic participation, leisure and consumption) is a transformation disrupting the social world in all its forms (Couldry 2016). Statistics confirm the assertion that the datafication of almost everything is growing relentlessly: in 2012 it was claimed that 90% of the world’s data had been created in the previous two years (IBM 2012), and a future 40% annual rise in data generation has been estimated (Manyika et al. 2011).

Less commonly noted is the place of everyday experience in the machine of datafication. The Berliner Gazette (nd) has claimed that 75% of these newly available data are by-products of people’s everyday activities, and Michael and Lupton also note the centrality of the everyday in the current Big Data moment:

Human actors contribute to big datasets when they engage in activities such as making calls and using apps on mobile phones, using online search engines such as Google, purchasing goods or services online or taking part in customer loyalty programmes, uploading contributions to social media platforms, using wearable self-tracking devices or moving around in spaces that are equipped with digital sensing or recording devices (Michael and Lupton 2015, 104).

Despite the significance of such everyday practices in the production of large-scale data, little attention has been paid to people’s thoughts and feelings about these data-producing processes. These issues have not, on the whole, been the focus of the emerging field of data studies, which seeks to understand the new roles played by data in times of datafication. This is a problem for a number of reasons. First, if we do not understand whether data condition everyday experiences as it is claimed, and our thinking about these matters is not informed by the perspectives of the people upon whose data datafication is built, scholarship about data-in-society will be incomplete. Second, and importantly for this special issue, in the absence of such knowledge, data activism, which seeks to challenge existing data power relations and to mobilise data in order to enhance social justice, will rely upon the judgments of elite technical actors and activists about what would constitute more just data practices. In contrast, I argue that to build a picture of what just data arrangements (that is, the practices of organisations that handle and produce data, the policies that govern these practices, and provisions for the development of skills that people need in order to engage with data) might look like, it is important to take account of what non-expert citizens themselves say would enable them to live better with data, based on their everyday experiences of datafication. Greater understanding of everyday living with data can contribute significantly to the knowledge base on which data activism is built. A third problem, then, is that by not focusing on these issues, the field of data studies is not currently as well aligned to the aims of data activism as it might be. This paper explores how we might address this gap.

The paper proceeds to elaborate the argument that data studies and data activism could be better aligned through a focus on everyday experiences of datafication. The next section provides a brief sketch of the field of data studies, identifying the everyday as a critical absence. Here I discuss how more empirical research into what it means to live with datafication could enhance both data studies and data activism. In the subsequent section, I outline what the project of researching living with data might look like. I explore two possible approaches to this endeavour, the first of which I describe as “a phenomenology of datafied agency.” The second focuses on data-related capabilities and their emotional dimensions. Both of these approaches, I argue, suggest the need for a vocabulary of emotions in researching everyday living with data.

Inserting the Everyday into Data Studies and Data Activism

Within the emerging field of data studies, datafication is said to have all kinds of effects, many of them troubling, and to result in an array of new harms. These include: increased surveillance; threats to privacy; new forms of algorithmic control; and the expansion of new and old inequalities and forms of discrimination. Surveillance is said to be much more ubiquitous, opaque and speculative in datafied times, as social media and other kinds of data mining make it possible to surveil aspects of life once private and intimate (Andrejevic and Gates 2014, Dencik and Cable 2017, Lehtiniemi 2017) and thus deny people their basic right to privacy (Cohen 2013). Privacy itself is a contested issue, with industry figures like Mark Zuckerberg claiming that it is no longer a social norm (Johnson 2010), and critical researchers pushing back against this view with all kinds of empirically grounded (boyd 2014) and philosophical (Nissenbaum 2009) assertions that privacy does, in fact, still matter. Elsewhere, significant attention has been paid to the function of algorithms in emergent forms of datafied governance and control. In times of datafication, algorithms have power, it is claimed (for example by Gillespie 2014 and Striphas 2015). They make and shape data in particular ways, certifying knowledge and so shape public, social and cultural life.

Another troubling consequence of datafication is that it reproduces old inequalities and creates new ones. One of danah boyd and Kate Crawford’s much-cited “six provocations for big data” is that “limited access to big data creates new digital divides” (boyd and Crawford 2012, 673). In data mining, who is deemed to have expertise determines who controls the process and the “knowledge” about the social world that results, knowledge which in turn reproduces the social world, as scholars writing about the power of algorithms also claim. Relatedly, and emerging from these debates, the discriminatory consequences of the rise of big data have also been noted. Data mining, analysis and subsequent discrimination result in certain groups having better access to all kinds of resources (Andrejevic 2013, Taylor and Richter 2017). Datafication affects citizens differentially, and data-driven discrimination can mean that already-disadvantaged populations have their access to fundamental human rights further limited (see for example Gangadharan 2012 and 2015).

To date, studies of the rise of datafication have primarily sought to expose these harms, and the field of data studies has therefore been dominated by critical political economy and neo-Foucauldian analyses of the problems that accompany widespread datafication and its intricate relationship with neoliberal forms of governance. Data are seen as powerful and troubling actors in the control of contemporary life, playing a role in shaping how we live, what we know and how we know, and contributing in new ways to old problems like discrimination and inequality. Without doubt, this literature has played a vital role in making visible the serious issues that datafication raise in relation to rights, freedoms and justice, and in questioning the celebratory rhetoric that has accompanied the spread of big data.

But there are some absences here. Because data studies has primarily focused on the operations of data power and their harmful consequences, it has been characterised in large part by a focus on powerful actors. Studies which focus instead on attempts to democratise data, such as open data initiatives (Baack 2015), hackathons (Gregg 2015, Irani 2015), the Quantified Self (QS) movement (in which participants use apps and mobile devices to collect data about various aspects of their bodies and lives, such as the work of Neff and Nafus 2016) or data activism, also often focus on elites, as the initiatives and practices which are the object of these studies tend to involve technological and data savvy experts. For example, as early researchers of the QS movement have acknowledged, QSers are atypical in their data expertise, commitment and enthusiasm, and so cannot be said to be representative of “ordinary people.” Indeed, my use of the term “ordinary people” in the cultural studies tradition in this paper highlights what is missing from the existing literature. Building on the politics of the ordinary, my argument here is that “lowering” academic sights (McCarthy 2008) to “activities in the daily round” (Silverstone 1994) is a much-needed political gesture in the field of data studies.

Thus data studies has not paid much attention to the everyday experiences of non-expert citizens of living with data and datafication. Michael and Lupton noted in 2015 that “there is still little research that has investigated what the public make of big data, aside from reports from privacy organizations and government bodies” (Michael and Lupton 2015, 110), and that characterisation is still largely true two years later. It is because of this absence that, alongside Michael and Lupton, commentators such as Couldry and Powell (2014), Pink et al. (2017) and Ruckenstein and Pantzar (2015), have called for more research into everyday engagements with data. I join them in this call in this paper, argue that such a turn is important to data activism as well as to data studies, and explore how we might enact it. Indeed, it is because of these limitations in dominant critical approaches that I propose that the term “data studies” is preferable to describe this emergent field, rather than the alternative “critical data studies” which is also sometimes used (for example by Iliadis and Russo 2016).

It should be noted that there are a small number of exceptions to my claim that data studies has not attended to the everyday. These include research into the datafication of health, such as Harris et al’s (2016) work on cybergenetics, and the work of Lupton (2018) and Ruckenstein (2016). Another example is Couldry et al’s (2016) Storycircle project, which explored how analytics are used by community groups for social ends. Research in the field of data-driven discrimination, such as Eubanks and Gangadharan’s Our Data Bodies project (http://www.odbproject.org/), is also an exception, as it grounds concerns about the discriminatory effects of datafication in empirical research with those most likely to be discriminated against. Two small-scale studies have explored how social media platform users feel about these technologies and their algorithms (Bucher 2017, Colbjornsen forthcoming). Barassi’s Child Data Citizen project (http://childdatacitizen.com/), which aims to look at how the lived experience of childhood is being transformed by datafication, is also an exception, as is research into the everyday self-monitoring practices that have emerged from the more elite QS movement mentioned above (especially the work of Pink, such as Pink and Forst 2017, Pink et al 2017 and Pink et al 2018; see also Sharon and Zandbergen 2016). These examples notwithstanding, there is limited research which seeks to develop understanding of how ordinary people experience and live with data as part of everyday life – these exceptions represent only a small handful of projects from across the globe, after all. In short, we need more empirical research into everyday living with data.

A further problem with existing data studies literature is that much of it conceives of life with data in limited ways, as harmful and oppressive. This is not helpful for data activism which, as noted above, seeks to identify and establish more just forms of datafication. Data activism is characterised by mobilisations against existing data uses and practices, and has been defined as a “series of sociotechnical practices that, emerging at the fringes of the contemporary activism ecology, critically interrogate datafication and its socio-political consequences” (Milan and Van der Velden 2016, np). As such, data activism is premised on the assumption that current “data arrangements” are harmful to non-powerful citizens, and that alternative arrangements are therefore needed to improve people’s experiences of datafication. Data activism thus requires the possibility of agency, yet there is little scope for agentic engagements with data in the visions of datafication provided in much data studies scholarship.

Bringing the sociology of the everyday into data studies can help to resolve this problem. In sociological terms, the everyday refers to the habits and practices in which we engage and which surround us, what Pink et al describe as the “routines, contingencies and accomplishments” of the mundane (Pink et al 2017). Given that datafication is now widely considered to be a defining feature of everyday life (Couldry 2016), this approach is clearly helpful for understanding everyday experiences of datafication. Because the everyday is conceived as contingent and situational within this sociological sub-field, this makes it possible to think of social phenomena like datafication as not simply constitutive of social life, as critical political economy and neo-Foucauldian analyses do, but rather as “made and unmade” (Neal and Murji 2015, 812) through everyday practices. (Of course, people’s everyday lives are not all the same, and it is important to take account of how social inequalities lead to different data experiences, as work on data and discrimination has begun to do (such as the Our Data Bodies project mentioned above)). Thus not only does researching everyday engagements with data fill an empirical gap for data activism, its acknowledgement of the agentic actions that constitute social and political life mean that it is well aligned with data activism’s interest in the possibilities for agency in datafied times.

There is a third contribution that a focus on the everyday can make to data activism, in addition to the two already noted (that is, filling an empirical gap and focusing on agency). In a talk about data activism which had as its subtitle “the conditions of possibility for democratic agency in the datafied society”, Stefania Milan (2017) identified three such conditions. The first is critical consciousness, or “conscientização”, a Portuguese term coined by Brazilian popular educator Paolo Freire to refer to achieving critical understanding of the world and taking action against injustices revealed through such consciousness (Freire 1968). The second, according to Milan, is grassroots data literacy, which is required in order to comprehend existing data practices and processes. The third is critical imagination, or the capacity to imagine alternative forms of living with data. Here Milan draws on Emirbaye and Mische’s (1998, 970) definition of political agency as constituted “through the interplay of habit, imagination and judgement” (my emphasis). But what precedes the achievement of these conditions outlined by Milan? What comes before them? We need to know the answers to these questions before we can understand what leads people (or future data activists) to be able to acquire them. Here again, understanding of non-expert citizens’ experiences of datafication is crucial, because we cannot arrive at the conditions identified by Milan without understanding first how people who do not (yet) consider themselves as data activists experience datafication, and second how to move from these experiences to engagement in data activism. In the next section, I explore two possible approaches for arriving at such understanding and some of the issues that might emerge along the way.

Approaches to Researching “Living with Data”: Two Possibilities, and an Emergent Vocabulary of Emotions

One of the main purposes of exploring how ordinary people experience datafication in their everyday lives is to develop understanding of their perspectives on how they might live better with data, understanding that is useful to the mission of data activism and its efforts to improve data arrangements so that they are less harmful and more just. Taking account of what people say about these issues is important, but so are the conceptual tools with which we develop this knowledge. In this section, I explore two possible approaches that put philosophical concepts into dialogue with examples from my own research. Taken together, these explorations suggest the need for a vocabulary of emotions in researching living with data.

A Phenomenology of Datafied Agency

The field of the philosophy of technology, concerned in large part with questions about the relationship between technology and well-being, seems a good place to start exploring how people live and how they might live better with data. A major question for this field is whether technological developments of diverse kinds are good or bad for society – as Brey (2012) notes, whether it is possible to lead good lives in a world so committed to technology is a pressing question. While some philosophy of technology literature focuses on subjective variations of well-being, either asking whether technology can make us happy (Spahn 2015), or conflating well-being with happiness and using these terms interchangeably (Søraker et al. 2015), subjective notions like happiness are not helpful to the cause of data activism and the data justice it seeks to promote. They are individualistic and do not contribute to thinking about the kinds of data arrangements that might benefit communities of people disadvantaged by current conditions.

We need therefore to turn elsewhere in the field, to philosophers of technology who ask whether technological ensembles of all kinds can be appropriated as tools of democratisation, enablement and activism, despite their origins within the belly of the beast. Data activism asks these same questions of datafication, a new kind of technological ensemble. This concern is captured nicely in the first words of Andrew Feenberg’s preface to Transforming Technology: “must human beings submit to the harsh logic of machinery, or can technology be fundamentally redesigned to better serve its creators?” (2002, v). Through this question about agency, important both to data activism and to researching datafied everyday life, Feenberg raises the issue of differential technological subject positions that he addresses explicitly elsewhere. In another book, Feenberg (1999) argues that the fundamental difference “between the dominant and subordinate subject positions with respect to technological systems” (1999, x) is significant, a distinction which echoes my argument that non-expert citizens’ experiences of datafication are differentially socially stratified. “Ordinary people encounter technology as a dimension of their lifeworld” (1999, x), he writes, and he continues:

For the most part they merely carry out the plans of others or inhabit technologically constructed spaces and environments. As subordinate actors, they strive to appropriate the technologies with which they are involved and adapt them to the meanings that illuminate their lives. Their relation to technology is thus far more complex than that of dominant actors (which they too may be on occasion) (1999, x).

Differential subject positions matter, Feenberg argues, because change comes “when we recognize the nature of our subordinate position in the technical systems that enrol us, and begin to intervene in the design process in the defence of the conditions of a meaningful life and a livable environment” (1999, xiv). Likewise, my argument here is that moving beyond critical thinking about technology, as Feenberg advocates, and exploring technology’s “ambivalence”, or “the availability of technology for alternative developments with different social consequences” (1999, 7), is an essential component of data activism and an important next step for data studies.

Feenberg’s concerns are concerns about agency, as are those of data activism. To address these issues of agency and possible change through empirical research into everyday experiences of datafication, a phenomenological approach might be helpful. Phenomenology focuses on the point of view of actors and their perceptions and experiences of the (datafied) world – in this sense it is distinct from ethnography which is more commonly dependent on the point of view of the observing researcher. This perspective and phenomenology’s excavation of the taken-for-granted layers of everyday action fit with the project that I am describing here, which also prioritises the conscious experiences of datafication of non-expert citizens. As such it enables attention to the differences in people’s experiences of datafication which, as noted above, are significant. Schutz and Luckmann’s (1973, 105) argument that phenomenology acknowledges that “not only the what but also the how of the individual situation in the lifeworld belongs to the fundamental elements of the stock of knowledge” (quoted in Couldry et al 2015, 125), further confirms its usefulness for producing a more detailed understanding of living with data “from the bottom up” (Couldry and Powell 2014). Also arguing for a phenomenology of datafication, Couldry et al (2015) state that we need research “that recognises people’s ongoing reflexivity about their conditions of entanglement with [the] digital infrastructures” of datafication (2015, 124) – indeed, Couldry et al and some of the other researchers turning their attention to living with data who were noted above (such as Ruckenstein 2014) describe their own research as phenomenological. For many writers, such reflexivity is intricately entangled with agency. Couldry, for example, defines agency as “the longer processes of action based on reflection, giving an account of what one has done, even more basically, making sense of the world so as to act within it” (Couldry 2014, 891). Phenomenology’s focus on both of these things, reflexivity and action, make it a useful approach for researching living with data.

My research into uses of social media data mining, in organisations which I describe as the pillars of everyday life (local councils, museums, training organisations, educational institutions and shops), illustrates what a phenomenology of datafied agency might offer. This research focused on the datafication of working life, not on non-experts’ experiences of data in the everyday, but nonetheless it is indicative of what might surface through such an approach. In this research, one thing that emerged across research sites was a desire for numbers, which, I have argued, engaging in data mining elicits (Kennedy 2016). I give some examples below, after which I reflect on what these tell us about reflexivity, agency, and the importance of researching living with data.

In research with city councils and museums in which we experimented with social media data mining techniques, the data generated through our experiments was met with much enthusiasm by participants, especially when presented in visually-appealing charts and graphs. A sense of amazement was expressed by participants who read reports we produced and who attended workshops. Emotional responses to data often elicited a desire for more data. One participant said:

I think I had a lot of confidence in the numbers. I think I was amazed by how deep a lot of these tools could go. […] I think they’re very clever. It was amazing how much you could drill into this.

Some participants said that they were required to report the results of analytics exercises “up” to managers and funders, but that there was no discernible action taken as a result. Quantitative data, produced through systems like Museum Analytics (a platform which carries out social media analytics for the museums sector, as the name suggests), were desired by managers and funders, with no apparent concrete consequence – the “data gathered” box was ticked, the desire for numbers was fulfilled, and data were filed away.

In interviews within social media insights companies, some social media analysts said that accuracy was not important to their clients. One said:

Whether that data is accurate or not is irrelevant. They just want some numbers to put into a PowerPoint that they can show to their boss. If anyone asks, “Are you keeping an eye on social media?” You can say, “Yes, we’re 36 this week.” And it is a very attractive solution.

Sometimes clients are drawn in by the allure of numbers and just want numbers, participants said – inaccuracy is acceptable, as long as the desire for numbers is fulfilled. Participants felt that this desire for numbers suppressed discussion between them and their clients about the limitations of the data that data mining produces, and they were frustrated at this. They were alert to the inadequacies of the numbers they produced and they would have liked to talk to clients about the challenge of obtaining good quality, accurate data, and about what the numbers that social media analytics produce do and do not represent.

In an interview in an educational organisation which uses the services of social media analytics companies, a digital marketer expressed his frustration with what he referred to as “the fetishism of the 1000”. He claimed that within his organisation there was a perception that the ability to cite numbers of people “reached” through a campaign was proof in itself of a campaign’s success. When a project has been completed, he said, if numbers can be produced, if it can be claimed “that we’ve reached 50,000 and we’ve had 1000 people respond back to us about it, then that fulfils some kind of sense of requirement.” He felt that measurement was rarely undertaken with a genuine desire to self-evaluate, but rather was motivated precisely by a desire to produce numbers, which were uncritically equated with success.

The emotions that came to the surface in this research seem troubling, as it seems as if there is no escape from the prevalent desire for numbers in the datafied workplace. However, a phenomenological approach, which excavates the layers of conscious experience and is attentive to the judgements, perceptions and emotions of key actors, reveals a more nuanced picture. As can be seen in the examples above, although a desire for numbers dominated, participants were also reflexive about the ways in which such desires limit critical engagement with data amongst their clients or colleagues. There was also some reflection about the ethics of data mining amongst participants, about whether data mining methods are acceptable, whether the mantra that social media data are public and therefore “fair game” to be mined and analysed holds up. All of these reflections, and related ethical decision-making and line-drawing, can be seen as nascent acts of agency and point towards possibilities for different data arrangements, as participants did not simply submit to the “harsh logic” (Feenberg 2002, v) of datafication and desiring numbers. A phenomenological approach to participants’ experiences also highlights the important role that emotions play in engagements with data in the workplace. This in turn suggests a need for a vocabulary of emotions in researching everyday experiences of datafication.

Data-related Capabilities and Their Emotional Dimensions

Another way in which we might bring together data activism’s interest in social justice with people’s perspectives on how they live and might live better with data is through the capabilities approach. Two of the original proponents of this approach, Amartya Sen and Martha Nussbaum, argue that to think about how people might live well, we need to focus on what people need to be capable of doing, should they choose to (Nussbaum 2006 and 2011, Sen 1973, 1992 and 2009). For Nussbaum, the capabilities approach directs attention to how certain social or institutional arrangements are more effective than others in enhancing social life and social justice. Both Nussbaum and Sen emphasise the role of the external environment in enabling capabilities; this makes possible a normative assessment of technological developments like datafication, argues Johnstone (2012). Nussbaum’s version of the capabilities approach also involves asking how we might live well together, especially in light of the growing value attached to competitive individualism and neo-liberalism. Despite the dominance of these latter ideas, within capabilities thinking, “the notion of the common good survives as a key ethical principle” argues Hesmondhalgh (2012, 84), a notion that is clearly relevant to data activism.

For Sen, capabilities are freedoms (freedoms to – for example receive an education, earn a living, express oneself, form relationships – and freedoms from – for example oppression, violence, censorship, arbitrary arrest) (Johnstone 2012). He defines capabilities as “the various combinations of functionings (beings and doings) that a person can achieve […] reflecting the person’s freedom to lead one type of life or another […] to choose from possible livings […]” (Sen 1992, 40). Nussbaum (2006) lists ten such capabilities, some of which are physical, such as not dying prematurely and having good health and shelter. Others relate more clearly to datafication: being able to imagine, think and reason; being able to form a conception of the good and to engage in critical reflection about planning one’s life; and having control over one’s (political or material) environment, can all be influenced in one way or another by people’s experiences of data and datafication.

A number of media theorists have applied the capabilities approach to explorations of the media practices that can enhance people’s efforts to live good lives (such as Coleman and Moss 2016, Hesmondhalgh 2013, Mansell 2002). For example Hesmondhalgh’s (2013) Why Music Matters draws on Nussbaum and Sen’s ideas to reflect on the communicative practices – in this case, in relation to music – that would create conditions under which humans might flourish. Whilst it might be easy to understand why cultural goods like music matter in relation to living well and flourishing, these questions are equally important in relation to data and datafication.

Exploring how ordinary people’s experiences of datafication might be enhanced through the lens of the capabilities approach could also be seen as a response to Andrew Sayer’s argument that ideas about living well and flourishing are vital for attempts to understand how greater social justice might be achieved (Sayer 2011). Sayer argues that social science should be more attentive to people’s first-person, evaluative relation to the world, to their evaluations of how they live and how they might live well, because “Social struggles are not merely struggles for goods and power but about how to live, about what is a just, virtuous or good life and a good society” (2011, 172). Social science often disregards people’s evaluative relation to the world and the force of these evaluations, he claims, but we need to attend to these things; values, feelings and emotions need to be taken more seriously in social sciences. Sayer writes that there is a “macho tendency to view the study of values, emotions and ethics as less scientific than the study of power, discourse and social structure”, whereas he argues that we need to develop our understanding of the former which, he argues, constitute “ethical being in everyday life” (Sayer 2011, 15).

 

Ideas about capabilities and flourishing might help us to understand what is important for people to live their lives well with data. In relation to datafication, capabilities might include being able to have control over one’s own data, to choose to opt out of – or, better still, in to – data gathering, and to make sense of data mining processes because they are made transparent to non-expert citizens, or accountable to expert others. The problem is that, whilst these issues are widely discussed amongst data activists, ordinary people’s perspectives on whether they might result in living better with data are missing from these debates. This is why we need to produce the kinds of first-person evaluations of “living well with data” that Sayer advocates. 

As with the phenomenological approach discussed earlier, a capabilities approach also highlights the importance of emotions in relation to living well and flourishing. Through this approach Hesmondhalgh looks at how music communicates emotions in particular ways and how emotions thus play a role in good lives. Likewise, Sayer highlights the significance of feelings in everyday evaluative relations with the world. In other research of mine which explored how people engage with visual representations of data which circulate in the everyday, my co-researchers and I found that emotions play important roles in such engagements (Kennedy and Hill 2017, Kennedy et al 2016). Because a major way that most people access data is through their visual representation, as Gitelman and Jackson (2013) argue, visual sensibilities are required in order to make sense of data, not just cognitive reason and statistical skills. This entanglement of the numeric and the visual, at the heart of most people’s engagements with data in their everyday lives, means that data stir up emotions. A broad range of emotions emerged in relation to engagements with visual representations of data in the research, including pleasure, anger, sadness, guilt, shame, relief, worry, love, empathy, excitement, offence. Participants reported emotional responses to visualisations in general, represented data, visual style, the subject matter of data visualisations, the source or original location of visualisations, and their own skill levels for making sense of visualisations (Kennedy and Hill 2017).

Thinking about the relationship between emotions and capabilities can contribute to understanding experiences of engaging with visual representations of data. In our research, participants expressed strong feelings about their own skills – or capabilities – for decoding visualisations. Some participants felt a lack of confidence in this regard. One said of a visualization: “It was all these circles and colours and I thought, that looks like a bit of hard work; don’t know if I understand”. Many of our participants expressed similar negative feelings about their lack of skills, and this lack of confidence had a profound impact on some participants’ engagement with visualisations. One reacted to all of the visualisations that we showed him in a focus group with confusion and dislike, as seen in the grid he produced, on which we asked all participants to place thumbnail images of visualisations in order to identify whether they liked, or had learnt from, the visualisations which we showed them. He placed most thumbnails in the “disliked + didn’t learn” quadrant, and one outside the grid altogether (Figure 1).

Figure 1: Seeing Data participant’s grid

This participant responded in this way because he doubted his own ability to make sense of visualisations. This lack of confidence was echoed by other participants, such as this person who wrote in her diary about visualisations in a newspaper article on tax:

I felt confused and a bit stupid for not being able to stay the course with this article. It’s too maths based for me. Too many numbers and pie charts, I get lost in it.

Feeling “stupid” was the result of a perceived lack of skills, such as knowing how to read particular chart types. When participants felt more skilled, unfamiliar chart types could evoke positive emotions, rather than negative ones: “I didn’t hate it because it made me want to try and put a little bit of effort into navigate those lines”, said another participant about a visualisation of freshwater consumption across the globe.

Educational background was an important factor which influenced whether participants felt stupid or felt capable, something which they themselves recognised. Some participants identified that higher education contributed to the development of relevant skills. One participant who had a Masters degree was more confident about how to understand and assess the data visualizations than the participant whose grid is shown in Figure 1, because he felt he had the training to do so. Alongside education, gender and class, which we might describe as social arrangements, to use Nussbaum’s term, appeared to influence emotions such as feeling confident or feeling stupid.

Although these examples are not concerned with reducing data-related inequalities and improving data justice as data activism seeks to, the emotional dimensions of living with data which they reveal are relevant to this mission. As Sayer notes, it is important to take people’s values, emotions and ethics seriously in our quest for social justice. Feelings play a role in non-experts’ experiences of data and datafication processes. Building on this finding, for example in initiatives which aim to enhance data literacy (Milan’s second condition for the possibility of datafied agency), might result in more people feeling confident about their skills for engaging with data, and greater understanding of datafication and its many consequences might result. Thus the approaches I explore in this paper, and the ways in which they enable foregrounding the emotional dimensions of living with data, are important for data activism as well as for data studies. To advance, both fields need better understanding of how data and emotions relate to each other in ordinary people’s everyday experiences of datafication.

In thinking with concepts like capabilities and living well with data, it is important to subject them to critical scrutiny, of course. For example, how might data be constrained from contributing to people’s flourishing, given their location in unequal relations of power? How to account for how injustice, inequality and oppression inform people’s access to capabilities, to living well and flourishing (Hesmondhalgh 2012)? How to avoid homogenising everyday experiences, and instead to recognise that human needs “may take very different forms in different societies” (Hesmondhalgh 2012, 18)? Who gets to decide what needs to be sacrificed for the common good? It is important to acknowledge that these notions are not without problems – they are abstract and complex and suffer from what Johnstone (2012) describes as “radical empirical underspecification.” Nonetheless, their application in data studies and in data activism could open up a vocabulary of the emotional, and such a vocabulary could make these issues explicit and open them up for debate. Thus these concepts, and the vocabularies they bring with them, are potentially useful for understanding datafication’s social consequences. They might help our mission to identify the types of data arrangements that can enhance people’s efforts to live good lives, reduce data-related inequalities and improve data justice.

Conclusion: Why This Matters for Data Activism

Datafication is a major social phenomenon which has all kinds of effects, and because of this, how people experience data in their everyday lives is extremely important. Yet, in data studies and in data activism, little attention has been paid to ordinary people’s thoughts and feelings about their own data production and the data practices of others. Understanding such everyday experiences is crucial, because without such understanding, data studies and data activism are not informed by the perspectives of the people with whose lives they are ostensibly concerned. In this paper I have argued that we need to look at datafication as it is lived, felt and experienced at the level of the everyday. We need to attend to ordinary people’s perspectives on how data arrangements can be improved, so that these perspectives can play a role in determining improved data arrangements. Through this, I argue, an empirical gap in data studies would be filled, knowledge that is useful for data activism would be produced, and data studies and data activism would be better aligned. Data activism seeks to challenge unjust data arrangements and to mobilise data in order to enhance social justice, and taking account of what non-expert citizens say would enable them to live better with data will help data activists to imagine more just arrangements, the third of Milan’s conditions for data activism.

I have also argued that inserting the everyday into data studies opens up a space in which to explore possible conditions for agency in datafied societies, to paraphrase the subtitle of Milan’s talk. The topic of agency is a shared concern of scholars of the everyday and of data activists, and in this way a focus on the everyday makes another contribution to data activism. What’s more, not only does researching everyday experiences of datafication fill an empirical gap and highlight issues of agency, it also enables us to attend to the question of what precedes data activism. How is datafication lived, felt and experienced by non-expert citizens before they start to develop the conditions or consider the possibility of activism in relation to data? Data activists cannot make the three conditions for datafied political agency identified by Milan possible without first understanding where we need to move from in order to get to these conditions.

In this paper, I explored two approaches to researching everyday engagements with datafication. The first, “a phenomenology of datafied agency”, mobilises a phenomenological excavation of data experiences to explore the possibility of agency in datafied conditions. The second is an approach which looks at data-related capabilities and their emotional dimensions, and which highlights the importance of identifying what people need to be capable of doing in order to live well in times of datafication. Arguably, the “conditions of possibility for political agency in the datafied society” identified by Milan are, in fact, capabilities. Thus there is a direct link between these approaches and data activism. Both of the approaches I discuss highlight the importance of emotions in everyday engagements with data. The ways in which these approaches value emotions in relation to acting agentically and living well can help us understand the important role that feelings play in everyday engagements with data. These approaches thus suggest the need for a vocabulary of emotions within data studies and data activism, something that has been largely absent from these fields to date.

At the time of writing, there are many examples of initiatives which aim to improve life with data. Attempts to “do good with data”, to paraphrase the strapline of US visualisation agency Periscopic, include enterprises like DataKind, which puts data experts, working pro bono, together with social groups to address social and humanitarian problems, for example relating to homelessness and child poverty. Data visualisation itself is seen by many practitioners as a way of “doing good with data” (this is the strapline of a datavis agency after all), as this practice is often motivated by a desire to make data transparent and accessible. Efforts to develop alternative, human-centric personal data management models, like the MyData movement (http://mydata2016.org/) and to enhance data literacy, such as School Of Data (https://schoolofdata.org/), can also be included here. What is missing from these endeavours is an understanding of non-expert citizens’ perspectives on whether they might result in better living with data. We need to listen to the voices of ordinary people speaking about the conditions that they say would enable them to live better with data and, in so doing, arm ourselves with knowledge which advances data studies, serves the interests of data activism, and brings both fields into closer alignment with each other.

 “Someone once said it is easier to imagine the end of the world, than it is to imagine the end of capitalism,” wrote Frederic Jameson, in a now famous reflection on the stifling parameters of cultural life in late capitalist societies.

Building on this, Mark Fisher advanced the concept of “capitalist realism” in his 2009 book of the same name as a way to articulate the peculiar persistence of a system that has proved itself so full of fallacies, so unjust and inegalitarian in its rewards (Fisher 2009). Writing in the wake of the financial crisis, Fisher described the contemporary condition of capitalist realism as a ‘‘pervasive atmosphere” that comes to regulate thought and action, limiting the possibilities of even imagining alternatives: “[it is] the widespread sense that not only is capitalism the only viable political and economic system, but also…it is now impossible even to imagine a coherent alternative to it.” (Fisher 2009: 6; italics in original). In describing capitalism in these terms, his ambition was to stress its contingency and destroy its appearance as an inevitable ‘‘natural” order.

The dominance and resilience of contemporary capitalism has been a prominent theme in debates on the politics of imagination, and linked to that, the field of social and political imaginaries (Adams et al. 2015). In looking at imagination and imaginaries, we are invited to consider how we make sense of society, instituting and instituted by social practices in their emergence, formation and reproduction. As such, a concern with the politics of imagination is as much a concern with the way in which social institutions and practices are legitimized and continued as it is a concern with the possibilities for the articulation and doing of alternative formations. That is, imagination can both open and close a path to critique (Bottici and Challand 2011).

Capitalism, as a political-economic imaginary in the context of modernity (Adams et al. 2015), is intimately linked to contemporary forms of surveillance. Today, capitalism is said to increasingly progress through an accumulation logic based on the ability to monitor and track different forms of social activity with the view to predict and modify human behavior as a means to gain revenue and market control. An information order Zuboff (2015) has described as ‘‘surveillance capitalism”, advanced in the form of ‘‘big data”, and underpinned by a digital economy based on mass data collection and analysis. Turow et al. (2015) have argued that this information order now constitutes what can be described as a ‘‘21^st century imaginary” in which we see the discursive and institutional normalization of surveillance infrastructures pervading more and more aspects of everyday life. Despite prominent concerns with how these infrastructures might be inherently unjust, Turow argues that ubiquitous and continuous data collection has become ‘‘common sense” – a set of practices that people have become widely resigned to.

In this article I engage with this interplay between data-driven surveillance and contemporary social imaginaries, using research based on the aftermath of the Snowden leaks, first published in June 2013, which revealed unprecedented details of contemporary surveillance programs. Drawing on Fisher’s use of the term ‘‘‘realism” in relation to capitalist realism, I advance the argument here that public debate and response to the Snowden leaks indicate a similar ‘‘pervasive atmosphere” that comes to regulate thought and action, in which the active normalization of surveillance infrastructures limits the possibilities of even imagining alternatives – a condition I describe as surveillance realism. I also use this to highlight some of the opportunities and challenges in articulating and doing resistance, such as the kind that has emerged in the form of ‘‘data activism” (Milan and van der Velden 2016). Whilst the research is focused on the UK and the Snowden leaks, I also draw here on wider debates and studies to illustrate key developments. By analyzing activities and public response in this way, the point is, in line with Fisher, to ‘‘reveal what is presented as necessary and inevitable to be a mere contingency” as a way of advancing an emancipatory politics that can also then ‘‘make what was previously deemed to be impossible seem attainable.” (Fisher 2009: 21).

I start by briefly outlining the role of imagination in sustaining and challenging social orders before going on to discuss contemporary imaginaries of surveillance and datafication, providing examples from post-Snowden developments. Based on this, I argue that surveillance realism is useful for understanding the politics of imagination in relation to ubiquitous data collection, and I end by considering how resistance might be enacted in such a context.

The Politics of Imagination

Early inceptions of imagination, such as those articulated by Aristotle and later within the Kantian tradition, predominantly viewed imagination in relation to individual capacity. It is with the emergence of social imaginaries as a field that we begin to emphasize the properly social aspect of the imagination, and to grasp it as authentically creative rather than as merely reproductive and imitative (Adams et al. 2015). In his seminal work on the imaginary institution of society, Castoriadis (1987) stresses the collective instituted and instituting forms of meaning and the societal dimension of the human condition. Imagination, for Castoriadis therefore, is linked more to a social imaginary that comes to be central for the very existence of society, in that the instituting social imaginary is always at the same time instituted. Coming before both the concept of ideology and utopia as presented in the related work of Ricoeur (Bottici and Challand 2011, Adams et al. 2015), Castoriadis presents a view of imagination as radical, in the double sense that without it there could be no reality as such, and that it can always potentially question its objects by disclosing possible alternatives.

In advancing a concept of a social imaginary, ‘‘reality” and ‘‘subject” become intertwined. As laid out by Taylor (2004, 2), the social imaginary is, therefore, not a set of ideas; rather, it is what enables, through making sense of, the practices of a society. For Taylor, this is central to understanding the nature of modernity and the conception of moral order of society that accompanies it. He describes the relationship as a form of ‘‘embeddedness” in that certain self-understandings are embedded in certain practices that are both promoted by the spread of these practices and which shape them and help get them established. It is, as he argues, ‘‘both a matter of identity – the contextual limits to the imagination of the self – and of the social imaginary: the ways we are able to think or imagine the whole of society.” (2004, 63). The focus is on the way ordinary people ‘‘imagine” their social surroundings, shared by large groups of people, if not the whole of society, leading to a public understanding that makes possible common practices and a widely shared sense of legitimacy.

In that sense, a focus on imagination in this collective form helps to understand how systems come to be legitimized and what comes to be perceived as possible. Whilst the concepts of imagination and imaginary have been used much more differentially than this (including a prominent discussion on socio-technical imaginaries in the field of Science and Technology Studies), my interest in them here is in terms of how people come to make sense of social institutions and practices. I therefore draw on the field of social imaginaries as outlined by Adams et al. (2015, 19) as elucidating ‘‘the ways in which cultural configurations of meaning creatively configure the human encounter with – and formation (as articulation and doing) of – the world, on the one hand, and, articulate their centrality for the emergence, formation and reproduction of social institutions and practices, that is, of social change and social continuity, on the other.” In particular, I draw on the notion of radical imagination, in Castoriadis’ sense, as both the study of what limits our imagination and the study of what expands it. Similarly, Ricoeur’s outline of the reproductive imagination as the core of ideology and utopia situates the ideological imagination as that which reproduces an image that society has of itself, whilst the utopian imagination produces alternative images of society that puts ideological images into question (Ricoeur 1986). This can come to serve an emancipatory politics by approaching the legitimacy of established practices and the accompanying cultural articulations as socially constructed. The way in which we imagine our social surroundings is not necessary or inevitable. Emancipatory politics serves to reveal the power relations that underpin any perceived natural order as a means to simultaneously nurture alternative imaginations of what can be possible.

Although Fisher, in his description of capitalist realism, does not refer explicitly to the field of social imaginaries, he is concerned with what he describes as a ‘‘pervasive atmosphere”, articulated in the circulation of cultural products and evident in work and education, in which capitalism has ‘‘colonized dreams”, lowering our expectations into accepting that, in the words of Margaret Thatcher (during the miners’ strike) ‘‘there is no alternative’. As Fisher states, ‘‘The “realism” here is analogous to the deflationary perspective of a depressive who believes that any positive state, any hope, is a dangerous illusion.” (2009, 9). This is closely aligned to more recent debates on the resilience of contemporary neo-liberal capitalism and the relative lack of political imagination as in the articulation of alternatives. There has been a closure of the economic imaginary in the form of depoliticization, meaning the active denial of the need for a political, public discussion of the means and ends of the market economy (Adams et al. 2015, Straume and Humphrey 2010). This also speaks to Foucauldian notions of normalization as the way in which norms of conduct are established and enforced through discursive practices backed up by institutional sanctions (Foucault 1977). However, Fisher’s take on ‘‘realism” pays more explicit attention to the active marginalization of alternatives, and the dictation of terms of any resistance. That is, the focus is on the construction of the realm of what is considered to be possible.

In thinking about how people make sense of society, and understanding Fisher’s use of the term ‘‘realism” to articulate a relative closure of cultural articulations of the world as part of an active denial of political debate, I now turn to look at data-driven surveillance through this lens.

Surveillance Culture and Dataism

In analyzing the changing consumer environment of the twenty-first century, Turow et al. (2015) combine Taylor’s notion of social imaginary with Gramsci’s concept of hegemony to articulate the entrenchment of data-driven surveillance within retail. For Gramsci, hegemony is a way to understand how power is exercised without, or in addition to, coercion. It relies on a ‘‘compromised equilibrium”, achieved from concessions that subordinate groups gain from the bourgeois state, which is then maintained through the concrete coordination of interests by civil society. Civil society – the public sphere where ideas and beliefs are shaped – in turn comes to reproduce hegemony through the ‘‘organic intellectuals” in the media, universities and religious institutions that then enable ‘‘common sense” societal values and legitimacy (Gramsci 1971, Wood 2015). Whilst hegemony therefore emphasizes how common sense becomes institutionalized as part of certain social mechanisms and power relations, Taylor’s social imaginary captures the nuances of everyday life and practices. Combining these approaches, Turow et al. (2015) argue that, over time, consumers have become institutionalized into accepting a retail environment that has transitioned from broad demographic lenses to one based on their monitoring as individuals who give off streams of data, often in real time. Consultancies and technology firms assist retailers in reshaping the shopper, the store, and the deal so that people (consumers) become institutionalized into what Turow et al. understand as taken-for-granted values, habits and expectations of an increasingly data-driven and discriminatory market-place.

In his later book on the topic, Turow (2017) builds on his account of ‘‘common sense” data collection by drawing on Jackson’s 1968 notion of the ‘‘hidden curriculum”, used to refer to the social norms and rules in education that become accepted through repetition and the implicit values laid out in schools that connects young people to the structures of power in society and defines their relationships to them. This can be broadened to the education that people receive through media and culture about all institutions (Gerbner 1972), including, as Turow argues, the retail space. There is a prevalence of symbol systems that designate, for example, the meaning of trendy clothing, outdoor happiness, and wealth – codes that shoppers have come to take for granted. We are now, Turow states, ‘‘on the cusp of a retailing era that is adding an entirely new layer of routine surveillance activities and that carries with it the accompanying underlying lesson that it is common sense for shoppers to accept individualized profiling and deal making as part of the process of buying things.” (2017, 18).

Whilst Turow’s focus is particularly on data-driven surveillance in the retail space, his analysis of the normalization of surveillance infrastructures in everyday life finds echoes far beyond it and are pertinent in relation to post-Snowden debate more broadly. Indeed, when the documents leaked by Snowden were first published in June 2013, they both confirmed and surprised prevalent understandings of surveillance practices. The documents detailed a continuation of the development of what had previously been described as the “surveillance society” (Rule, 1973; Lyon, 1994), but they also represent a significant juncture in how surveillance is conceptualized and discussed. Classic conceptions such as Foucault’s ‘‘panopticon” or Orwell’s ‘‘Big Brother” struggle to account for these technological developments and later incarnations of surveillance practices (Browne, 2015). Moreover, concepts such as the ‘‘surveillance state” and ‘‘surveillance society” are no longer adequate for describing the form contemporary surveillance takes. As was made explicit in the Snowden leaks, the state is no longer the only, perhaps not even the main, arbiter of surveillance. Instead, we are confronted with what Harcourt (2015, 66) describes as a new ‘‘oligarkhia” made up of an ‘‘amalgam of the intelligence community, retailers, Silicon Valley, military interests, social media, the Inner Beltway, multinational corporations, midtown Manhattan, and Wall Street.” This ‘‘oligarkhia” is the product, in part, of shared interests in security (from foreign corporate espionage, cyber hacking, malevolent actors etc.) among government and technology companies, in conjunction with the rise of neoliberalism and the associated trend toward deregulation, outsourcing and privatization.

For Lyon, the contemporary nature of surveillance also cannot be sufficiently understood in terms of ‘‘surveillance society’, which he understands as a concept originally used to indicate ‘‘ways in which surveillance was spilling over the rims of its previous containers – government departments, policing agencies, workplaces – to affect many aspects of daily life.” (2017, 826). What is missing from these accounts are the active roles played by surveillance subjects, paying more attention to the ways in which citizens, consumers, employees etc. experience and engage with surveillance. Rather, Lyon suggests, in line with Turow’s analysis, we need to understand surveillance within everyday practices and in the very fabric of society’s culture. By advancing the concept ‘‘surveillance culture”, therefore, Lyon is seeking to highlight how surveillance is becoming part of a whole way of life: ‘‘From being an institutional aspect of modernity or a technologically enhanced mode of social discipline or control, it is now internalized and forms part of everyday reflections on how things are and of the repertoire of everyday practices.” (Lyon 2017, 825).

In placing emphasis on subjects as active participants in surveillance, Lyon points to a more complex power dynamic than have previous discussions of surveillance which focused mainly on the ways in which surveillance is exercised from ‘outside’, by one actor over another. Rather, we need to consider how different ‘‘surveillance mentalities and practices” come to be manifested (Lyon 2017, 828). This speaks to the active participation by citizens in how data is generated which marks part of the human interaction with digital environments. That is, data is collected based on what might be described as ‘voluntary’ activities by ‘ordinary citizens’, who ‘choose’ to share data about themselves. Or as Harcourt (2015, 19) argues, perhaps not so much out of actual choice, but rather ‘‘a feeling of necessity”. Digital infrastructures lure us into participating in data extraction not just in their ubiquity but also in the ‘‘seductive surveillance” that marks their technological manifestations (Troullinou 2016). The technologies which end up facilitating surveillance, Harcourt (2015) argues, ‘‘are the very technologies that we crave.” Harcourt goes as far as to argue that we now live in a society of exposure and exhibition; an ‘‘expository society” that takes the architectural structure of a mirrored glass pavilion in which we are not only seen but in which ‘‘we play and explore, take selfies and photograph others” (Harcourt 2015, 107).

Entire populations are integrated into systems of tracking and monitoring, continuously and in real-time; what Andrejevic (2017) describes as a shift from panoptic modes of surveillance to ‘‘environmental surveillance” – the replacement of selective disciplinary surveillance with total perpetual monitoring and on-going intervention. Power in such a society, Harcourt (2015) argues, circulates by a new form of rationality, one that is driven by algorithmic processes based on a ‘‘digital doppelgänger logic” in search of our data double. That is, the continuous collection of data abstracted from the digital traces left behind as we interact with our digital environments is used to identify, classify, assess, sort, or otherwise ‘‘control the access to goods and services that define life in modern capitalist society.” (Gandy 1993, 15). Indeed, as Van Dijck (2014, 198) has outlined, ‘‘metadata appear to have become a regular currency for citizens to pay for their communication services and security – a trade-off that has nestled into the comfort zone of most people.” We are seeing the gradual normalization of this datafication as a new paradigm in science and society. Such normalization is driven by the ideology of what Van Dijck refers to as ‘‘dataism”. She describes this as showing

characteristics of the widespread belief in the objective quantification and potential tracking of all kinds of behavior and sociality through online media technologies. Besides, dataism also involves trust in the (institutional) agents that collect, interpret, and share (meta)data culled from social media, internet platforms and other communication technologies. (Van Dijck 2014, 198).

In outlining dataism as the ideological component of the datafication paradigm, Van Dijck highlights how this paradigm is being advanced on a set of assumptions that are deeply contested. Not only is there an assumption that (objective) data flows through neutral technological channels, but also that there is ‘‘a self-evident relationship between data and people, subsequently interpreting aggregated data to predict individual behavior.” (2014, 199). These assumptions are further embedded in society through the active attempts to maintain the integrity of the system via government regulation aimed at assuring public trust in (private) data infrastructures by limiting excesses and harms.

Surveillance Realism Post-Snowden

The Snowden leaks and its aftermath are an important component in further outlining the nature of this datafication paradigm. Having served as contractor for the National Security Agency (the NSA), Edward Snowden gained privileged access to information about secret surveillance programs run by the NSA and British Government Communications Headquarters (GCHQ), amongst other agencies. In 2013, he leaked this information to leading global media organizations. Starting in June 2013, organizations such as The Guardian, the Washington Post, Der Spiegel and The New York Times began to publish detailed and wide-ranging stories revealing the unprecedented extent to which our activities and behavior in digital environments are tracked, monitored, analyzed and stored. While the leaks focused on surveillance by state agencies, they also highlighted the ‘‘oligarkhia” of state, corporate and commercial actors mentioned above.

Whilst protests were prevalent in the immediate aftermath of the Snowden leaks, in particularly in the United States with the Stop Watching Us protests, and in Germany with the Freedom Not Fear protests, dominant interpretations have suggested that there was little response from the broader public concerning these revelations of surveillance (Cable 2015). Often this has advanced a narrative that people either do not care, or feel that surveillance practices are largely justified in a perceived trade-off between security and privacy (Mols and Janssen 2017). Yet such an analysis is too simple and neglects the complexities of the contemporary digital environment. In this section I build on the above arguments with regards to shifts in the nature of data-driven surveillance, focusing particularly on the immediate aftermath of the Snowden leaks. I advance the concept of surveillance realism as a way to articulate the context in which we are to understand public debate and responses to the revelations of mass data collection and analysis. I use this concept to describe the nature of acceptance and resignation in relation to the increasing mass collection of data across social life and the active marginalization of alternatives, despite widespread unease and concerns about these infrastructures and systems.

The justification and normalization of data-driven surveillance has been actively advanced in public debate. When the Snowden leaks were first published, we saw a quick convergence across media, government and security services around a discourse of threat and (in)security. In the UK, condemnation of both Snowden as a whistle-blower and The Guardian newspaper for publishing the documents was prevalent across political parties, and state agencies responded with overt force to stop information from being released. This became symbolized in the now notorious confrontation at The Guardian where editorial staff was forced to destroy hard-disks and files under the watchful eye of the British intelligence agency, GCHQ, in a feeble attempt to prevent further publications of the Snowden files. Within months of the first publications the sitting director of MI5 made a rare public statement condemning the newspaper, accusing it of ‘‘handing the advantage to the terrorists” (Whitehead 2013). This argument was replicated in mainstream media debate with the majority of newspapers advancing the opinion that the publication of the Snowden leaks had compromised the work of the intelligence services (Wahl-Jorgensen and Bennett 2017). In fact, editor of The Independent Chris Blackhurst went as far as to publish an editorial explaining that he would not have published the leaks had it been up to him, stating ‘‘if MI5 warns that this is not in the public interest who am I to disbelieve them?” (Blackhurst 2013).

As Wahl-Jorgensen and Bennett (2017) have illustrated, the media debate on surveillance and data collection following the Snowden leaks became marked by an overarching discourse of securitization that situated surveillance firmly within a terrorism context. This is despite the fact that journalists themselves are often critical about surveillance practices, acknowledging not only the increasing normalization of datafication, but also raising concerns about the limited public knowledge of the extent of data collection and critiqueing the media’s contribution in providing justifications for it (Hintz et al. 2018). However, as Wahl-Jorgensen and Bennett (2017) point out, data-driven surveillance was discursively justified by stories about the Snowden revelations and its aftermath through the reliance on official sources expressing the view that surveillance should be increased or is acceptable/necessary; the most frequently expressed opinion in newspaper coverage. Sources expressing this view suggested that surveillance is crucial to national security, and is particularly important to strengthen in light of terrorist threats. As Wahl-Jorgensen and Bennett state, ‘‘the prominence of opinions that justified surveillance in the name of national security in mainstream media is not accidental. Rather, there is evidence to suggest a longer-standing legitimation of state interventions through a reference to concerns about state security in the British context.” (2017: 10). That is, as they put it, the idea of national security constitutes a discursive ‘‘trump card” overriding all other claims. This narrative was intermittently supported with statements from intelligence agencies which claimed that surveillance played an active role in curbing terrorist attacks (cf. Bakir 2015).

In such a context, a widespread logic that this also means mass surveillance is primarily a concern for those who have ‘‘something to hide” (i.e. terrorists, criminals, and other social deviants) became manifest. As Wahl-Jorgensen and Bennett (2017) suggest, the ‘‘nothing to fear” position ‘‘offers a common-sense articulation of the idea that being under constant surveillance is not only a fact of life in contemporary societies but also entirely acceptable given the constant terrorist threat.” Whilst the Snowden leaks constituted a global media event in which coverage differentiated across social, historical and political contexts with reference to national and geopolitical interests (Kunelius et al. 2017, Hintz et al. 2018), the ‘‘nothing to hide, nothing to fear” pretext as a way of describing public responses and attitudes has been prominent across national contexts (Lyon 2015, Mils and Janssen 2017). Although blogs and alternative media provided a space for more critical arguments that highlighted a lack of transparency surrounding intelligence agencies and violations of privacy, ‘‘the mediated public debate on the issue has, in the longer run, contributed to rendering such concerns less visible and marginalized” (Hintz et al. 2018, 77).

Research on public attitudes to post-Snowden data collection highlights that whilst many people actually have these concerns, the justification for surveillance is often widely internalized (Dencik and Cable 2017) along with a trust in institutions that collect data, as pointed out by Van Dijck (2014). This is not to suggest that people are either passive or apathetic in relation to their digital environments, but rather that attitudes and practices are continuously negotiated in relation to the way data-driven systems have become integrated and mediated in society. In fact, numerous studies have shown that people feel a ‘‘lack of control” over how information is collected (Eurobarometer 2015) and are ‘‘bewildered and fearful” about the use of their data (Gompertz 2016) but do not necessarily act according to such concerns. In explaining the ‘‘privacy paradox”, for example, Hargittai and Marwick (2016) emphasize pragmatism as a central component. This is the paradox that emerges from a prominent concern with privacy in the digital environment that is not manifested in actual online behavior. Focusing on young people in particular, they outline how people experience ‘‘privacy fatigue” and confusion about the data-driven systems in place, which leads to an acceptance of their data being collected as a pragmatic response in the negotiation with digital infrastructures. Moreover, research has shown that, despite an increasing awareness of surveillance and a prevalent unease with the implications of such systems, people feel largely disempowered to fundamentally challenge the nature of data collection (Dencik and Cable 2017). Rather, they come to negotiate their own position and vulnerability as part of an everyday practice within what they recognize as being relatively limited parameters, such as adjusting privacy settings on social media, or refraining from sharing certain content or not engaging in particular searches (Marthews and Tucker 2015, Penney 2016, Hampton et al. 2014).

In other words, the sheer ubiquity of surveillance infrastructures and their embeddedness in ordinary aspects of social, political and cultural participation make it difficult to think they can be challenged. Adjusting to this ‘‘reality” pragmatically is a key tenet of what Draper and Turow (2017) term a ‘‘sociology of digital resignation”. Crucially, for Draper and Turow, a sociology of digital resignation suggests that these developments are not natural or inevitable, nor that people are simply passive agents in the process. Rather, in addition to the nature of public debate discussed above, they stress the ways in which resignation to mass data collection has been actively manufactured through a number of different practices, such as obfuscation in privacy agreements between users and platforms, or simply by making services inaccessible if personal data is not shared. These defaults are ingrained in the general standards and design of digital infrastructures and are advanced in the operations of data mining practices. At the same time, people continuously navigate this environment, negotiate costs and benefits, adjust settings where pragmatically possible, but with the recognition that any actual control over the environment is limited. In recognizing the extent of a prevalent ‘‘surveillance culture”, as Lyon (2017) suggests, such resignation also illustrates the increasing struggle to actually imagine alternatives. Rather, in line with Fisher’s ‘‘realism”, expectations become lowered, and data-driven surveillance, along with its perceived infringements upon civic rights, becomes a small price to pay for being protected from terrorism, or for being able to participate in society through digital means. This surveillance realism is a realism that speaks to a hampered imagination where datafication and surveillance is seen as the only legitimate response to social ills.

Whilst the Snowden leaks provided opportunities for reflection and substantial reform on data collection, any substantial overhaul of digital infrastructures was kept at bay. Although new legislation was introduced in places like Brazil with the Marco Civil Act, and changes to the Freedom Act in the US intended to curb surveillance powers, any fundamental questioning of surveillance, and indeed of a data-driven digital economy underpinning contemporary surveillance culture, was undermined. In fact, several countries have extended surveillance powers and enabled further data collection following the Snowden leaks, with the UK’s Investigatory Powers Act leading the way (Hintz and Dencik 2016). The political discourse on surveillance in the aftermath of the leaks restricted the policy debate within very limited parameters and served to advance hegemonic powers. Disputes circulated around definitions of surveillance (e.g. at point of collection vs. point of analysis), infrastructure security and the need for encryption, and the nature of state-corporate relations in the sharing of data (Hintz and Brown 2017). However, political consolidation around the justification for mass data collection marginalized possibilities for fundamental opposition. In what can be considered atypical fashion, digital rights and civil liberties groups were invited to the table through consultations, and were allowed to participate in the policy process, but their participation became predominantly token in nature, being allowed to provide expertise and winning some battles on specific aspects of surveillance policy at the expense of any fundamental review of surveillance practices and mass data collection. In what can be considered a ‘‘compromised equilibrium”, in Gramscian terms, that comes to stabilize surveillance as ‘‘common sense”, Hintz and Brown point out that the recognition of campaign groups and advocacy organizations as legitimate actors ‘‘has enabled civil society to participate in a key policy process, but it has also risked the normalization of surveillance as principled opposition is replaced by collaboration, and it has exposed differences in civil society agendas.” (2017, 794).

These developments speak to a context in which the Snowden leaks, and their aftermath, whilst creating awareness and unease with digital infrastructures, also illustrate the perseverance of surveillance culture and dataism. When Fisher described capitalist realism, he wrote of the credit crisis of 2008: ‘‘The speculations that capitalism might be on the verge of collapsing soon proved to be unfounded. It quickly became clear that, far from constituting the end of capitalism, the bank bail-outs were a massive re-assertion of the capitalist realist insistence that there is no alternative.” (2009, 78). In this spirit, the aftermath of the Snowden leaks, in terms of both policy and technology developments (just think of the proliferation of Artificial Intelligence, Internet of Things, ‘‘smart” cities and ‘‘smart” homes), largely enabling further and wider data collection and data sharing, has made clear that Snowden’s revelations did not constitute the end of surveillance. In fact, the Investigatory Powers Act was, to use Fisher’s words, a massive re-assertion of the surveillance realist insistence that there is no alternative.

Beyond Surveillance Realism?

So what power does imagination have in a state of surveillance realism? Imagination has the potential for both oppression and emancipation; it may limit or expand that which we see as possible. As Castioradis as well as Ricoeur and Taylor make clear, a concern with the creative and collective dimensions of imagination as social imaginaries is also a concern with the ways the instituted order of society is problematized in the search for ‘‘the possible” as opposed to ‘‘the given” (Adams et al. 2015). Any established social order always includes resistance, and the aftermath of the Snowden leaks also included continued and new challenges to surveillance realism. However, the nature of resistance in any instituted order is also partly generated and shaped by the circumstances of that society. In his analysis of the anti-capitalist movement at the time of Fisher’s writing, he describes the staging of protests as ‘‘a kind of carnivalesque background noise to capitalist realism” where the suspicion was that the actual aim was not to replace capitalism but to mitigate its worst excesses (2009, 13). Although Fisher has been criticized for succumbing to ‘‘Left melancholia” (Hoffman 2016), an interesting aspect is his concern with the rejection of political organization in formulations of resistance at the time, and a turn to moral critiques of capitalism that only reinforce capitalist realism. In discussing the limits of confining critique to the moral realm, he states: ‘‘Poverty, famine and war can be presented as an inevitable part of reality, while the hope that these forms of suffering could be eliminated easily painted as naïve utopianism.” (Fisher 2009, 20). Capitalist realism, therefore, according to Fisher, can only be threatened ‘‘if it is shown to be in some way inconsistent or untenable; if, that is to say, capitalism’s ostensible ‘realism’ turns out to be nothing of the sort.” (ibid.).

In her analysis of environmental movements in late capitalism, Naomi Klein (2014) has posited a similar critique. Whilst there has been an increased awareness of climate change and a substantial normative overhaul in media and political debate on the crisis of the environment (Castells 2009), there has also been an ongoing concern with ways in which to engage public response and resistance. A key issue has been the marketization of environmental concerns such as the notion that it is possible ‘‘buy yourself green” through more environmentally ethical consumption. This has, at one and the same time, attributed socio-economic status to environmentalism as an aspirational consumerist lifestyle whilst also individualizing the response. That is to say, the onus is on the individual consumer as the emblem of environmental resistance (Scott 2010). More broadly, Klein (2014) highlights the limitations of engaging with the environment as a question of moral conscience, or to frame it as an opportunity for competitive advantage in a market economy, instead of linking it more directly to questions of economic justice. ‘‘A different kind of climate movement”, Klein contends, ‘‘would have tried to challenge the extreme ideology that was blocking so much sensible action, joining with other sectors to show how unfettered corporate power posed a grave threat to the habitability of the planet.” (Klein 2014: 20).

In thinking about resistance to data-driven surveillance post-Snowden these concerns can inform a useful critique. Whilst there has been an increased awareness and important prominent developments in activism that has sought to challenge dominant trends of datafication (Milan and van der Velden 2016), resistance in the aftermath of Snowden has also been pursued through particular avenues. Most notably, these have been technological pursuits to self-protect against surveillance (what Milan and van der Velden refer to as ‘‘reactive” data activism) and lobbying around policy pertaining to privacy and data protection (Dencik and Hintz 2017). Forums to provide secure digital infrastructures proliferated in the wake of the Snowden leaks, with ‘‘numerous digital rights and internet freedom initiatives seizing the moment to propose new communication methods for activists (and everyday citizens) that are strengthened through encryption.” (Aouragh et al. 2015: 213). Increase in the use of privacy-enhancing tools such as the TOR browser, GPG email encryption, and encrypted messaging software such as Signal, indicate a rising consciousness and concern with surveillance practices. Alongside this, digital rights and civil liberties groups, such as Open Rights Group, Big Brother Watch, Article 19 and Liberty in the UK, have regularly issued statements regarding their concerns about surveillance, organizing public debates and lobbying legislators particularly around the Investigatory Powers Act and data protection regulation. This has been accompanied by litigation activism where groups have taken governments to court over particular policies, and made concerted efforts to change technical standards and protocols within relevant institutions and bodies, such as the Internet Corporation for Assigned Names and Numbers (ICANN) (Dencik et al. 2016).

These efforts have been significant in creating a contested environment for advancing mass data collection and for pushing back on certain surveillance practices. However, resistance of this kind has also struggled to challenge the wider social imaginary and provide a substantial threat to surveillance realism (Dencik et al. 2016). Partly, an issue with technological responses to mass data collection is the risk that they come to advance individualized understandings of resistance in which the onus is on the individual to change their own behavior. This means that challenging data collection becomes an individualized act based on perceived skill and ability to engage in privacy-enhancing digital practices, such as downloading encrypted software, using anonymised browsers, and changing security settings. Lobbying for policy reform and engaging in litigation activism, meanwhile, is often bounded by technical and issue-specific expertise that confines the debate to a small constituency of experts (Hintz and Brown 2017).

Moreover, resistance to data-driven surveillance following the Snowden leaks has often focused on trying to mitigate the excessive harms of datafication rather than questioning developments at a fundamental level. Gürses et al. (2016) highlight how this has led to digital rights campaigns centred on ‘targeted’ surveillance as a more benign alternative to ‘mass’ surveillance, and an emphasis on proportionality as the overarching goal. As Gürses et al. argue, this constitutes depoliticized framings that are unable to account for the ways in which surveillance has been historically central to the control of particular communities and as a way to limit and suppress dissent. Furthermore, such framings serve to entrench the constructed trade-off between privacy and security that underpins the surveillance realist narrative that mass data collection is, indeed, a necessary and inevitable part of contemporary society. Although these responses are shaped, in part, by what is perceived as possible, what is missed in these efforts is a form of resistance that explicitly highlights how datafication and data-driven surveillance relates to dominant economic interests and political agendas in advanced capitalist societies. These processes are neither accidental nor inevitable but serve a particular form of social organization. In such a context, identifying infringement upon individual privacy as the core harm produced by mass data collection may do little to reveal the power structures that shape digital infrastructures. And individual technological self-protection may do little to overcome or change them.

Rather, an analysis of data-driven surveillance as a ‘‘realism” invites a more systemic critique of datafication in which resistance is intimately linked to questions of social and economic justice; what has also been referred to as a ‘‘data justice” movement (Dencik et al. 2016). In advancing such a framework, the aim would be to situate data-driven surveillance in the context of the interests driving such processes, and the social and economic organization that enables them. For example, this involves engaging with the ways in which data collection and analysis embodies historical institutionalized forms of discrimination and exclusion that limits opportunity and participation for certain communities in society (Gangadharan et al. 2015, Eubanks 2018). Similarly, the asymmetries of power between those who collect and analyze data and those who are subject to such data collection and analysis as an inherent feature of datafication (Citron and Pasquale 2014), are seen as an expression of the increasing concentrations of power in fewer hands and related to a wider trend of privatization and deregulation (along with a shift in decision-making away from the public realm). Or, the corporate and centralized nature of data systems is understood in relation to the organization of the digital economy and the labour relations and governance that sustain it (Scholz 2017). In this sense, questions of economic and social justice precede any analysis and development of data infrastructures and their position in society.

Although there is not the space to outline it fully here, resistance, through this lens, would involve dynamic collaboration between different groups and movements in civil society that combine economic, social, cultural, ecological and technological dimensions in articulating both problems and solutions (see also Hintz et al. 2018). Pointing to surveillance realism in this context is therefore about inviting a more active politicization of data processes that, to borrow from Ricoeur’s (1986) assertions, articulates the ideological imagination as a way to nurture the utopian imagination (here I also take inspiration from writings on ‘‘real utopia” such as those of Olin Wright 2010). As Fisher notes, ‘‘nothing is inherently political; politicization requires a political agent which can transform the taken-for-granted into the up-for-grabs.” (2009, 79). Surveillance realism identifies contemporary (often undesirable) mass data collection as a contingency that has been actively constructed as an inevitability, which can therefore also be challenged and reconstructed.

Conclusion

In drawing on Fisher’s notion of ‘‘capitalist realism” as a way to understand the contemporary social condition with regards to data-driven surveillance, and the datafication paradigm more broadly, this article posits a way to reveal the contingency and construction of our current digital environment. The perceived necessity and inevitability of mass data collection is one that has been advanced partly through a compromised equilibrium, to use Gramsci’s term, in which the normalization and entrenchment of a surveillance culture has stabilized the nature of contemporary digital infrastructures as ‘‘common sense”. This, in turn, has established a social imaginary of resignation to ubiquitous data collection despite prevalent feelings of unease and recognition of discriminatory and suppressive effects. In a context of surveillance realism, the injustices and fallacies of the system become a small price to pay to fight off inefficiency, threats and terror.

The Snowden leaks constitute a significant moment in the advancement of surveillance realism. They provided unprecedented insights into the extent of datafication and created increased awareness of surveillance practices. This led to outbursts of resistance, which focused particularly on what we might think of as techno-legal solutionism. Such a response, however, has been unable to transform the social imaginary and pose a substantial threat to surveillance realism. Rather, it has struggled to overcome enclosed expert discourses and individualized acts of resistance that have been confined to mitigating the worst excesses of mass data collection, sometimes even advancing the inevitability of the model through its suggested compromises. The UK’s Investigatory Powers Act in the wake of the Snowden leaks has come to symbolize the re-assertion of the surveillance realist insistence that there is no alternative.

The inability to articulate a coherent alternative to surveillance culture and dataism speaks to the politics of imagination that is played out in the kind of ‘‘realism” that Fisher described. It is one in which our aspirations and hopes are formatted to fit the hegemonic system. In advancing a critique, therefore, suitable for an emancipatory politics, it becomes essential to destroy the ‘‘natural order” of surveillance realism in order to make what seems impossible attainable. That is, the challenge becomes one in which the issue is not simply to harvest the resources available to mitigate the excessive harms of the current datafication paradigm, but is one in which we have to expand the limits of our imagination and reassert the possibilities of another world, another way of organizing society.

Acknowledgements

The author wishes to thank two anonymous reviewers as well as Alex J. Wood for the very useful comments on earlier drafts of this article.

Introduction

The progressive datafication of social life is often perceived as a threat to democratic publics. Critics warn that filter bubbles would undermine dialogue and consensus, that social discrimination will be reinforced (Barocas and Selbst 2016) or that ‘surveillance capitalism’ is fundamentally anti-democratic (Zuboff 2015). Yet as “democratic power is calculated power” (Rose 1999, 200), datafication is also driven by democratic visions and closely linked to notions of political accountability, fairness, and citizen empowerment. Forms of datafication driven by commercial players or governments have always been accompanied by civil society actors or journalists who have utilized data and related forms of quantification to advance their goals. However, given the new structures of (data) power that shape the workings of governments and businesses today, Milan and Van der Velden (2016, 6) suggest that it is increasingly important to investigate “how activism evolves in relation to big data”. They argue that new forms of ‘data activism’ are “enabled and constrained by data […] and this special relation shapes tactics, identities, and modes of organizing” (Milan and Van der Velden 2016, 3).

In this article, I explore ways in which activism is enabled by datafication by looking at a group of actors who not only reacted to processes of datafication, but pro-actively embraced them: civic technologists. Civic tech is an umbrella term for diverse projects that attempt to make engagement easier for citizens, improve communication and feedback between governments and citizens, and strengthen political accountability. Among other things, civic technologists develop parliamentary monitoring websites, tools to help citizens report local infrastructure problems to local government, or freedom of information (FOI) websites that help users to submit freedom of information requests to public institutions. In its modern incarnation, civic tech is the result of a convergence between “communities of technological and political openness” (Yu and Robinson 2012, 195). Early examples include the British FaxYourMP (2003), which helped citizens to find and contact their representatives in UK parliaments (Townend 2008), or the monitoring website GovTrack.us (2004), which made information provided by the US Congress more accessible (Yu and Robinson 2012). From these early volunteer experiments, civic tech has grown substantially in recent years as it has been embraced by governments, corporations and foundations (Baraniuk 2013).

In addition to their success and growth, civic technologists are relevant because they act as pioneers for the use of data to facilitate civic engagement. While there have been predecessors of the tools they developed, the way they utilized data to make them accessible and offer additional services was novel. They took information that was available elsewhere and made it machine-readable, shared it openly, and built services on top of it, e.g. the ability to type in one’s post-code to find one’s representative in the British parliament on FaxYourMP. Through such practices, they combined concepts of legal and technical openness in new ways (cf. Yu and Robinson 2012). Being pioneers of this type of work is also part of the self-conception of at least some civic tech organizations. They possess a sense of mission and make teaching their (data) skills and experiences to other civil society or media organizations an important part of their work.[1] Finally, non-profit civic tech organizations such as the Sunlight Foundation in the US or mySociety in the UK were also among the first to advocate for open data policies, and supported related freedom of information policies (Schrock 2016).

However, despite civic tech’s success and potential influence, it has received little attention in media and communication studies to date. Most of the research that does exist is interested in how the phenomenon might reinforce existing power structures (cf. Gregg 2015), or who is using civic tech applications and in what way (cf. Cantijoch, Galandini, and Gibson 2016). What is missing is a nuanced understanding of the practices, ideas and motivations that guide civic technologists themselves and how those practices and ideas provide orientation for others. Critically examining civic technologists themselves is relevant because the broader impact of civic tech goes beyond the impact of individual civic tech applications. As Hepp (2016, 919) describes, pioneer communities such as civic technologists are influential in the sense that they develop “a horizon of possibility to which the everyday media appropriation of others orients itself, or at least can do so” (Hepp 2016, 919). Accordingly, we also need to be sensitive to how the practices and imaginaries civic technologists develop provide orientation for others, i.e. how other actors adapt and modify them.

This paper therefore critically examines how civic technologists understand and use data to “meet their social ends” (Couldry and Powell 2014, 2). What are the key practices of civic technologists in relation to data and how do they themselves understand what they are doing? Addressing these questions will contribute to our understanding of how activism is enabled or constrained by datafication, as it provides a basis for subsequent studies to examine if and how these practices and imaginaries can be found elsewhere, and how they might have been modified.

I present findings from a case study about the British non-profit organization mySociety. Founded in 2003, mySociety is one of the oldest and most influential civic tech organizations and arguably represents ‘best practice’ in the extremely diverse civic tech sector. Its UK websites have millions of users (mySociety 2015) and the organization had a direct influence on British policy-making (cf. www.parliament.uk 2014). Some of its more popular projects include FixMyStreet, which lets citizens report local problems like broken streetlights or potholes to local government; its right-to-know website WhatDoTheyKnow which helps users to submit FOI requests to public institutions; or its parliamentary monitoring website TheyWorkForYou which gives detailed information about voting records and makes parliamentary speeches more accessible. mySociety’s projects are also prominent internationally and customized versions of its tools are used in 44 different countries (mySociety 2015), which let mySociety to transition from a UK-centric to an international organization. While it is not representative of the phenomenon as a whole, mySociety’s success and international influence provide a good starting point for studying the values and practices which shape civic technologists’ use of data.

In the following, I shall first discuss my methodological approach. I follow a practice theory approach and use the concept of ‘imagined affordances’ (Nagy and Neff 2015) as a lens which helps amplify how members of mySociety themselves understand data and how they imagine it to advance their agenda. The remainder of the article describes how the data practices of mySociety relate to their broader imaginaries. In the conclusion, I will reflect on the implications for studying civic tech and data activism in general.

Researching the Imagined Affordances of Data

To examine how members of mySociety understand and use data to meet their own ends, this paper relies on the concept of ‘imagined affordances’ (Nagy and Neff 2015). According to Nagy and Neff (2015), whatever actions a particular technology enables or constrains does not solely depend on its features or its material properties, but also on the perception of users and designers. Both may have “expectations about their communication technologies, data, and media that, in effect and practice, shape how they approach them and what actions they think are suggested” (Nagy and Neff 2015, 5). Applied to the subject of this paper, what data enables civic technologists to do does not solely depend on the properties of the data they collect or re-use, or on the applications they are able to develop with it; it also depends on how civic technologists themselves understand and perceive how data can serve their agenda. These perceptions and understandings are the basis for the “horizon of possibility” (Hepp 2016, 919) they develop as a pioneer community, which affects the perceptions of other actors concerning how data can be used to facilitate forms of civic engagement and activism.

To study the imagined affordances of data for members of mySociety, this paper relied on a methodological approach inspired by practice theory. First, a focus on practices aligns well with the concept of imagined affordances because affordances enable or constrain certain actions, and people make sense of affordances “in and through practices” (McVeigh-Schultz and Baym 2015, 2). Second, practices are useful for examining the role of pioneer communities because they act as exemplars. This means that they not only communicate ideas and visions about how a technology can be used, but they also become influential because they demonstrate their own visions and thereby affect the perceptions of others. The practices they develop are expressions of their broader visions and we have to consider them inseparable if we want to understand their influence.

Accordingly, I employ methods that helped exploring what members of mySociety are doing and how they themselves understand and categorize what they are doing in relation to data (cf. Couldry 2004, 2012). I followed a constructivist grounded theory approach (Charmaz 2006). Grounded theory was chosen because of its core principle of theoretical sampling, i.e. an initial data sample is continuously expanded with new data to systematically elaborate and refine the theory. This approach was useful for exploring the open-ended range of practices (Couldry 2012) and to more fully reconstruct the perspectives of the research subjects without applying pre-conceived concepts.

Spread over three rounds of data collection, I conducted five semi-structured interviews with members of mySociety, including its founder and former CEO, two senior developers, a member of the international team, and a member of mySociety’s research team. These interviews had an average length of one-and-a-half hours. A large part of each interview consisted in the reconstruction of a particular project. Each interviewee was asked to pick a project that illustrates her or his work and then walk me through the development process: What was the initial idea behind the project, what were the different steps and phases for implementing the project, what happened after the initial release? Reich (2013, 422) calls this ‘reconstruction interviews’ because it reconstructs “technological ‘biographies’”. Exploring the development of a project in-depth vividly illustrated the practices and routines of my interviewees. If not mentioned by the interviewees, specific questions about the role of data were asked. After one project was explored, I asked whether this was a ‘typical’ project and if there are very different examples. If there were, I explored those as well. Other questions addressed self-understandings (preferred job title, understanding of civic tech) and personal or organizational ambitions and values.

These interviews were complemented by 17 documents found online: mySociety’s homepage (including the use of the Internet Wayback Machine to retrieve older versions), project specific websites, blog posts and forum discussions from Tony Bowden (who has been working for mySociety since 2009), other interviews given by different mySociety members to newspapers or bloggers (e.g. Townend 2008), as well as presentations given by members like Tom Steinberg or Dave Whiteland available online (UsNowFilm 2008; Arcopix 2014; IndigoTrust 2011; mySociety 2014). Some of these documents were included in the initial data sample, others were added later following the theoretical sampling. Moreover, I conducted ethnographic research on two separate conferences which were visited by several mySociety members: The Open Knowledge Festival 2014 in Berlin and the Mozilla Festival 2015 in London. These conferences helped to get an impression of the larger community mySociety is involved with and provided a helpful guidance for both the interviews and the analysis.

How mySociety Members Imagine the Affordances of Structured Data

In the following, I describe the imagined affordances members of mySociety hold around structured data. First, I will explore mySociety’s mission and self-understanding in more detail to give a dense description of the broader ambitions and imaginaries that drive this organization. Then I show how data is used to facilitate this mission by describing four imagined affordances: deep linking into documents to engage citizens with the processes of governments; making the performance of governments legible to affect how they implement laws and public services; affecting its users’ perceptions by demonstrating their impact to them; and scaling technological solutions to support a distributed form of agency.

mySociety’s Mission: Facilitating Engagement

mySociety’s self-proclaimed mission is to “help citizens demand better […] our web tools and apps are breaking down the barriers around governments” (mySociety 2016b). Its tools are supposed to give “greater access for citizens to the work of government and the democratic process”, which essentially means improving how publics can monitor and provide feedback to governments: “We believe that governments tend only to get better at serving the needs of citizens when citizens are capable of demanding better, creating a virtuous circle that leads steadily to better government” (Cridge 2015). While this statement might imply advocacy for specific policy changes, mySociety understands its role as a ‘mere’ facilitator of civic engagement, not as an advocacy organization.

Members generally reject the idea of being a gatekeeper that stands between the citizens and their governments. Instead, they suggest to provide the means by which others are able to take actions more effectively: “What we do is present the facts: This is how your MP [Member of Parliament] voted, this is where the money went, this is what was said. It’s then up to other people to do with that what they will, which might well be using it to promote a cause” (Interview: Research Team). The idealized and simplified scenario that members sketch out rhetorically to describe their role can be outlined as follows: Before mySociety enters the arena, citizens are apathetic and disengaged because engagement is too difficult and time-consuming due to high barriers raised by governments (in most cases unintentionally). mySociety identifies these barriers and then ‘drops’ its tools into the public arena to make engagement easier for citizens, which subsequently facilitates engagement between citizens and their governments and leads to better governance.

Underlying this approach is the assumption that more means for citizens to provide feedback to elected representatives leads to ‘better’ outcomes, i.e. more representative and therefore more democratic outcomes. Given its self-understanding as a ‘facilitator’, mySociety is not advocating for specific outcomes, but is concerned with the processes by which outcomes are generated: “We’re in favour of a vibrant, healthy, lively democracy. That means a rude and obnoxious place. Although we don’t want to do that ourselves, it’s entirely appropriate that we should facilitate other people to” (Steinberg quoted in Krotoski 2010). This approach builds on the principles of open source culture (Kelty 2008; Lewis 2012). “The essence of open source,” Weber (2004, 56) describes, “is not the software. It is the process by which software is created”. As the ideas and practices of the ‘open source process’ were increasingly applied outside of software development (most prominently with Wikipedia) they formed the basis for a larger technological and cultural phenomenon that Jenkins (2006) has described as participatory culture, a culture “which posits that knowledge is richest and most accurate when it reflects the pooled inputs of a distributed population, as opposed to the expertise of a single agent” (Lewis 2012, 847).

MySociety builds on previous forms of participatory culture and has particularly strong connections to technology-driven open data initiatives and rights-based open government or freedom of information initiatives, both of which are interested in applying the ‘logic of open participation’ (Lewis 2012) to institutionalized politics (Schrock 2016; Janssen 2012; Yu and Robinson 2012). mySociety was an early supporter of open data in the UK and its founder was part of a group that articulated the ‘8 principles of open government data’ (OpenGovData.org 2007). It also promoted freedom of information laws through its website WhatDoTheyKnow, which helps users to submit FOI requests to public institutions, and advocated for strong FOI legislations. Yet despite this strong connection, advocating for open data and FOI is not mySociety’s main purpose, they are rather perceived as “resources that mySociety needed to function” (Interview: Former CEO). This is because mySociety does not just build on participatory culture, it also extends it in important ways. Participatory culture relied on the connectivity of internet technologies to establish new forms of governance based on collaboration and sharing. As Lewis (2012, 848) describes, participatory culture is based on a forging of technology and culture, in that digitalization “enables greater user participation on a seemingly infinite order, and the socio-cultural context of this technology has encouraged greater participation to achieve normative aims of collective wisdom and well-being”.

MySociety similarly wants to create a more collaborative and participatory process for achieving better outcomes, but it does not solely rely on the connectivity enabled by internet technologies and ways of ‘coordinating collaborations’ (Kelty 2008). Its civic tech applications are not primarily about connecting people, but about facilitating them, i.e. enabling them to engage with governments in ways that go beyond ‘mere’ connectivity or access to information. In other words, mySociety is extending participatory culture by drawing attention to the conditions that would allow and encourage people to participate. Civic tech at mySociety is essentially about feasibility, in that it aims at making engagement more feasible for citizens by removing frictions such as needing to find out who represents them in parliament and how to contact them.

MySociety’s imagined affordances of data are closely tied to this broader mission of creating a participatory culture. To illustrate the fundamental importance of structured data for mySociety, a member uses the analogy of cooking ingredients:

If the useful thing is a cake that people want to eat, you’re interested in the ingredients…But you don’t want raw ingredients like wheat, you need the flour. Some processing has to be done to the ingredients before they are ingredients that you can sensibly make a cake with…Without the structured data, you wouldn’t be able to offer that service and until you can offer that service you couldn’t really prove that the demand for it would be so great. (Interview: International Team)

This suggests that structured data, if ‘served’ correctly, would increase engagement and subsequently alter the relationship between citizens and their governments because it shows that the ‘demand is great’. In the following, I unpack this statement and describe the imagined affordances of data underlying it.

Improving Engagement with Governments: Deep Linking

One of the most popular services on mySociety’s parliamentary monitoring website TheyWorkForYou are email alerts. For example, users can search for keywords in parliamentary discussions and then sign up to regularly receive emails informing them when their keyword comes up in future discussions. This service was significant because British parliaments used to publish transcripts of speeches as PDF files. To monitor keywords or what individual MPs are saying in parliamentary discussions, one needed to download these PDFs regularly and search through them individually. The email alerts at TheyWorkForYou turned this monitoring process into something people are able to do along the way, without investing considerable time and effort.

The key practice behind this service is the idea of ‘deep linking’: “The idea that there is councilor Jones who said ‘This is what we have to do in my home town!’ and you can cite it directly” (Interview: International Team) – similar to Twitter, where every individual Tweet has its own URL which can be shared or embedded on other websites. For deep linking, mySociety (2016c) considers transcripts “made of nicely structured data […] hard to beat”. If documents are in a format that does not allow deep linking, “you can’t cite, you can’t share, you can’t show specific utterances […] that pretty much stops details in documents being called out in debates” (Whiteland in mySociety 2016a). Today, it uses a data standard for modeling parliamentary speeches called Akoma Ntoso. It allows granular filtering (everything this person has said), an analysis of the speakers’ behavior (what was said, how and when?), and the ability to link speakers and what they say to other statements and events: “We would like to see people…build sites like ‘all public statements by the Prime Minister’” (mySociety 2016c). This creates a level of “semantic understanding” with an “awareness of speakers” (Interview: International Team) that is necessary for the services mySociety develops.

The ability to link deeply into a document using structured data is considered “really important in public discourses about documents” (Interview: International Team). More fundamentally, documents are considered important because mySociety is “concerned with the process of government and most government […] is actually the business of making laws, and laws traditionally have been written down” (Interview: International Team).

From the perspective of mySociety members, enabling deep linking into documents and providing services around it improves the public’s awareness of, and engagement with, the businesses of governments (as those businesses are captured in documents). As mySociety explains:

Transcripts are a kind of oil that greases the wheels of well-functioning societies. They let people discover when powerful people have made pronouncements that affect less powerful people. We believe that by making transcripts function better, more people will end up learning about decisions and opinions that affect their lives. (mySociety 2016c)

In the interviews, members also frequently referred to a claim by mySociety’s founder: “everything you can and cannot do in your life has been decided by more powerful people in a meeting” (cf. Whiteland in mySociety 2016a). Deep linking is about improving access to such meetings to help figure out “who was responsible for things” (Interview: Former CEO) by improving the accessibility of those documents which capture what was said by whom. Put simply, deep linking should help to keep track of where and when powerful people make decisions.

Taken together, mySociety is imagining the affordances of structured data in this case as a basis for a document-driven monitoring tool to help engage the public with the businesses and decision-making processes of governments. Through deep linking, structured data is imagined to “even [affect] an apathetic population, it affects the way that they behave and what they know about what’s going on in their own society” (Whiteland in Arcopix 2014). This makes turning documents into structured information a central part of mySociety’s mission of creating a more participatory culture. Importantly, deep linking is more likely to facilitate individuals who are already highly engaged and interested in the processes of governments to begin with, either privately or professionally (e.g. journalists or activist groups). As I will discuss below, mySociety also imagines data to help engaging citizens who are not necessarily interested in politics or feel disengaged and powerless.

Mediating between Governments and Their Publics

On FixMyStreet and WhatDoTheyKnow, mySociety collects all the problem reports or freedom of information requests by its users, tracks the responses by public institutions, and makes both publicly accessible. By making the resulting databases public, mySociety creates new forms of legibility and assessability. The database on FixMyStreet enables the analysis of a city’s infrastructure problems by the public and makes the performance of governments, e.g. how fast they fix problems in specific regions, legible. WhatDoTheyKnow similarly allows an assessment of how different public institutions respond to FOI requests and subsequently how the law is being practiced.

One reason for mySociety to ‘aggressively’ impose transparency on governments is its reliance on their cooperation. Its applications are built on top of services provided by governments and cannot exist independently without them. If public institutions refuse to cooperate, for example by ignoring reports sent via FixMyStreet, their tools would be of little or no use. By imposing transparency and allowing the public to assess their performance, mySociety makes it more difficult for authorities to ignore them. It is a way of pushing institutions to cooperate and to adopt mySociety’s emphasis on user-friendliness and accessibility (see below). This is described as “the one bit of activism that we occasionally engage in” (Interview: Research Team).

A less obvious aspect of this ‘one bit of activism’ is how mySociety’s use of data enables it to mediate between the bureaucratic and legal processes of governments and the users of its applications. The way mySociety’s tools work is usually not a direct reflection of how public institutions or legislations are working:

We think about the aim of the software as not being necessarily to model exactly the processes as they already exist in the world, but to make the software embody a slightly better way of doing things. (Interview: Senior Developer 2)

In this sense, mySociety’s tools are “intended to provoke some friction” (Interview: Senior Developer 2). In the cases of FixMyStreet and WhatDoTheyKnow, the laws and regulations did not prohibit the publication of problem reports or FOI requests, but it also was not something that was suggested. In addition, mySociety reinterprets how services and laws should be implemented in smaller ways, for example by forwarding reports to councils via email, while councils prefer people to use a form on their website; or by allowing users to send FOI requests to institutions that were not subject to the FOI law on WhatDoTheyKnow (mySociety 2011).

MySociety developer Tony Bowden (2010) described this principle as ‘dreaming out loud’: “simply act the way you want the world to be, then wait for reality to catch up” – by finding ways to make institutions cooperate and push ‘reality’ in that direction. This ‘dreaming out loud’ principle fundamentally depends on mySociety’s use of data to make the performance of governments legible and assessable for the public.

However, mySociety cannot simply invent new ways of how governments should work and then impose this vision on institutions. While they are intended to create frictions, the software tools and data structures mySociety develops need to reflect the real-world processes they are supposed to represent to a large enough degree “to actually work” (Interview: Senior Developer 2). Instead of ‘reinventing’ government, mySociety’s tools are compatible with existing services and legislations, but simultaneously utilize data to asses and affect how they are being implemented: “that gap between the way the law works, the way institutions implement the law and the way perhaps it might be ideal from a citizen’s point of view is an important one and one where I think it’s significant to build an artifact that demonstrates that” (Interview: Senior Developer 2). Using data to demonstrate and potentially close this gap is mySociety’s definition of ‘empowerment’: people are empowered by giving them tools which enable them to “see and be able to do what they are legally entitled to as easily as possible” (Interview: Research Team).

This understanding of empowerment encapsulates how mySociety imagines to affect the relationship between citizens and their governments through data. It uses data to facilitate the use of preexisting rights and services, not to fundamentally change them. It envisions its tools to act like a ‘layer’ that translates the bureaucratic and legal procedures followed by public institutions into user-friendly interfaces with accessible language for citizens. They are supposed to both reflect existing processes and visions of how these processes should ‘ideally’ work. The legibility and assessability created by data is imagined by mySociety to affect the implementation of rights and public services in ways intended to make engagement easier for citizens and thus ‘push reality’ closer to its vision of a more participatory culture.

Changing Perceptions: Providing a Sense of Agency

While facilitating already interested users via deep linking is important to mySociety (see above), it ultimately aims at engaging people who are usually not engaged. mySociety aims at a more long-term change in perception about “what is normal rather than what is exceptional” (Interview: Former CEO). Using TripAdvisor as an example, the former CEO explains that it “has caused a massive power shift in the hotel industry from the people who run the hotels towards people who stay in hotels”, even though it is not a “campaign for better hotels”. Services such as TripAdvisor would change people’s expectations about things like going to hotels if they are popular enough, i.e. widely used (cf. Steinberg in IndigoTrust 2011). In a similar vein, mySociety wants its tools to be “popular rather than idealistic” (Interview: Former CEO) and has a strong emphasis on usability, i.e. on making sure its tools are easy to use and provide “the same level of service that the best [commercial] websites we use nowadays would have” (Interview: Senior Developer 1).

In order to simultaneously achieve popularity and promote a more participatory culture, mySociety tries to ‘harness self-interest’ (Bowden 2014b) by generating “public good from private desire” (Steinberg in Nestoria 2008). It aims at developing services that address individual end-users, but use the data gathered from the engagement of these users to create “public value” (Steinberg in UsNowFilm 2008) on top of it. Crowdsourced databases such as those created on FixMyStreet or WhatDoTheyKnow are key to this. FixMyStreet helps individuals to fix their specific problem, but it also collects all the reported problems to create a public database about local problems that can be useful for others, for example local journalists who can sign up to get email alerts for problems reported in a specific region.

By bridging individuals with collectives in this way and by emphasizing ease-of-use, mySociety ultimately wants to affect the perception of users about their own agency. For example, when a problem reported via FixMyStreet has been fixed, it will be removed from the public city map and the user who reported it will receive an email encouraging her to report more, or to try out mySociety’s other projects for contacting her representatives. This way, mySociety is not only solving an individual’s problem, but is also trying to demonstrate the public value created by its action. This is intended to give users a “sense of agency…some ability to change their environment” (Interview: Senior Developer 2) by demonstrating that their actions do have an impact:

[W]hat we often see as apathy is really just learned helplessness. People feel powerless, because they don’t believe they can make a difference. And the best way to change that is not to argue with them […] It’s to simply show them that they do actually have power. That what they do can have an effect, not only for themselves but for people around them. (Bowden 2014b)

Here, the legibility and assessability of crowdsourced data is imagined to create a more abstract, cultural and psychological change in perception.

Taken together, the crowdsourced databases mySociety creates are imagined to affect both governments and the public. On the one hand, they enable mySociety to push governments and influence how laws and public services are being implemented, as they make the performance of governments legible in new ways (see above). On the other hand, it uses the data generated by its users in ways intended to change how they feel about engagement by providing easy-to-use tools and by demonstrating that their actions had an impact. Databases are imagined to affect emotions and perceptions in order to advance a vision of a more participatory culture, which illustrates the importance of emotions and perceptions around data (cf. Kennedy and Hill 2017). As I show in the next section, data is also key to mySociety’s ambition to support the development of similar applications elsewhere.

Scaling Civic Tech: Supporting a Distributed Form of Agency

The bulk of mySociety’s work today is based on collaborations with groups in other countries to create national versions of projects originally created in the UK, especially Alaveteli (an international version of its right-to-know site WhatDoTheyKnow) and tools for parliamentary monitoring. In part, this international orientation is driven by funding opportunities, but also because numerous groups in other countries expressed a desire to have customized versions of mySociety’s UK tools. While partly self-interested, members share the desire to support groups that similarly want to promote a form of participatory culture elsewhere. They think of themselves not merely as ‘tool suppliers’, but as part of an international civic tech community.

MySociety essentially wants to promote its values and practices in many different contexts by supporting local groups with developing customized versions of its tools. To achieve this, it has to accommodate the fact that “people in different places care about different aspects of politics. In some countries what really counts is how politicians vote, in others the crux is campaign finance contributions” (Steinberg 2012). Data is seen as both a key problem and a solution to this approach. One of the main obstacles for transferring an existing application to a new country or for building new civic tech applications from scratch is the design of consistent data models that adequately reflect the structures and legislations of the respective national government. A data model designed to capture the British parliamentary system cannot simply be transferred to another country. If they are not already available (as open data), developing such data models is complex.

Therefore, mySociety is one of the founding members of Poplus, a ‘global civic tech federation’ of organizations similar to mySociety, like Code for America in the US or g0v in Taiwan. The basic idea that drives Poplus is that “pretty much every tool in the civic and democratic space can be broken down into some parts that are universal, with usually only a little bit of local glue holding them together”:

Although pretty much every Parliament has different processes, they’re still dealing with the same raw ingredients – people, parties, speeches, bills, votes, etc. – and if we could create standardised ways of modelling each of those things, it would be a lot less work for people to combine these in the way that makes most sense for their own situation. (Bowden 2014a, emphasize added)

In connection to Poplus, mySociety supports data standards for modelling government structures and develops EveryPolitician, a project that literally collects data about every politician in the world. In this project, mySociety makes “data editorial decision[s]” (Interview: International Team) about how the ‘basic elements’ that can be found in every government are represented in a consistent manner across countries to ensure tools can be easily deployed internationally.

On a technical level, mySociety is trying to reduce problems of scale through data structures and tools that standardize how common ‘ingredients’ of government systems are formalized. However, members perceive this not merely as a technological detail, but as a way to facilitate mySociety’s vision of a more participatory culture elsewhere. By enabling other groups with similar values and social imaginaries to create their own, local versions of civic tech applications that serve their particular needs, mySociety is essentially trying to support a distributed form of agency. By promoting data standards and reusable tools, it tries to create the conditions necessary for supporting its values and approaches in as many different contexts as possible.

Conclusion: The Cultural and Historical Situatedness of Affordances

This article examined the imagined affordances members of mySociety hold around data to gain a deeper understanding of how civic technologists rely on data to meet their own ends. mySociety is trying to facilitate civic engagement and, by extension, create a more participatory culture. Taken together, it is imagining the affordances of structured data in ways that are supposed to expand the agency of publics towards governments: ways intended to enable citizens to better influence and interact with governments or other powerful institutions. Four imagined affordances have been identified.

First, members of mySociety use structured data to make ‘deep linking’ into documents possible, thereby aiming to increase the publics’ awareness of, and engagement with, the processes of governments. Second, crowdsourced databases enable mySociety to mediate between governments and their publics. By collecting data to monitor the performance of governments, and by making this data publicly available, mySociety is trying to push governments to cooperate with its services and thereby affect how existing laws and public services are being implemented in supposedly more user-friendly, i.e. ‘citizen oriented’ ways. Third, members of mySociety are trying to use data to affect the perceptions and self-understandings of their users. Crowdsourced databases are supposed to help connect the individual with the collective by demonstrating to individual users that their actions have an ‘impact’ and create a kind of ‘public value’. Fourth, members of mySociety are scaling technological solutions to support a distributed form of agency that should enable groups in other countries, which similarly want to promote a more participatory culture, to develop customized versions of mySociety tools that serve their particular needs.

The way mySociety is using data to apply notions of participatory culture to politics suggests that we should understand the role of civic technologists in the public arena as facilitators of engagement. Facilitating means that data is used in ways that are supposed to enable others to take actions themselves. mySociety does not advocate specific outcomes (such as particular policy changes), but is concerned with how the processes by which outcomes are generated are designed. It aims to enable users to easily engage with authorities in order to ensure that decision-making “reflects the pooled inputs of a distributed population” (Lewis 2012, 847). In other words, mySociety wants to influence the conditions by which others participate in the public arena without directing the public discourse or influencing policy making. At the same time, it sees its role as complementing actors who do want specific outcomes, like advocacy groups within civil society, or professional journalists who emphasize gatekeeping.

By showing how members of mySociety understand and use data to facilitate civic engagement and realize a more participatory culture, this article draws attention to the fact that “the political and democratic possibilities of data” (Milan and Van der Velden 2016, 8) cannot be determined in an abstract way. What data affords to whom does not only depend on the technological properties of data, but is fundamentally social, and both culturally and historically situated. This has implications for studying data activism in general and civic tech in particular. Related to data activism, the findings presented here invite us to extend Milan and Van der Velden’s (2016) conceptualization of data activism as creating novel epistemic cultures around datafication within civil society. The epistemic cultures and related social imaginaries promoted by mySociety are not novel: the practices and imaginaries described here build on participatory culture, which itself has roots predating computer culture and notions of open source software (Tkacz 2012). mySociety’s practices and epistemologies did not appear out of nowhere and the epistemic cultures they create are not necessarily novel, but they develop imagined affordances around data to implement these ideas in new ways. To understand imagined affordances, historical trajectories are just as important as the new elements added by data activists.

Studying imagined affordances does have a lot of potential for further developing and refining notions of data activism. First, imagined affordances provide a useful angle for moving beyond broad dichotomies of pro-active and re-active forms of data activism (Milan and Van der Velden 2016) and for studying how the epistemic cultures developed by data activists are connected to data in nuanced ways. Especially when combined with a focus on practices, it is a powerful tool for illustrating the “distributions of agency and organising forces” (Tkacz 2012, 404) activists set in motion. Second, the article shows how imagined affordances can be used as an integrative framework for studying how data activists affect the distribution of knowledge and power. I described how mySociety is trying to utilize the legibility and assessability created by data to change the perceptions of users about their own agency. Subsequent studies could examine how the practices and social imaginaries of data activists interrelate or clash with the self-understanding and perception of different groups in the public arena, and how different imagined affordances by various types of users emerge around civic tech applications.

Applied to civic tech, the research approach developed here calls for a more differentiated picture of the civic tech sector. Civic tech organizations and their funders are very much interested in a “coherent and clearly articulated vision and sense of shared identity for civic tech” (Donohue 2016). Yet while the diverse actors in the field of civic tech might align inasmuch as they are all interested in open data, reusability and a vague sense of improving ‘civic life’, their interests might eventually clash. For example, European non-profits like mySociety or the Open Knowledge Foundation Germany advocate for governments to copy civic tech applications. At mySociety, this even goes as far as stating that most mySociety projects “shouldn’t need to exist at all” (Interview: Senior Developer 1). Elsewhere, civic tech is closely aligned with start-up culture and attempts to create new industries. Wanting public institutions to copy civic tech applications in order to change the relationship between citizens and these institutions, or favouring the ‘government as a platform’ paradigm (O’Reilly 2010) and wanting public institutions to ‘step back’ and foster ecosystems of for-profit services, has very different implications for civic life (Baack in DataDrivenJournalism.net 2016).

Pointing out such differences is also important for developing a more differentiated critique of civic tech. Civic tech and open data initiatives have been said to uncritically support a neoliberal agenda driven by commercial interests rather than government accountability or citizen empowerment (cf. Bates 2012; Slee 2012; Gregg 2015). Moreover, their focus on technological solutions would merely reinforce existing power relationships by ‘empowering the empowered’ (Gurstein 2011). While this critique is important, it does not equally apply to every actor in the civic tech sector. We should not discard the agency and intentions of non-profit organizations such as mySociety, who are aware of these discussions and conduct research to better understand the impact of their applications. Members of mySociety constantly experiment and explore ways to support and encourage civic engagement. In this sense, they are “potential ‘laboratories’” (Hepp 2016, 929) that we should not ignore if we want to formulate normative principles for making processes of datafication more democratic and for creating more self-aware and agentic publics (Kennedy and Moss 2015).

The imagined affordances identified and described here can help to map differences among actors in the civic tech sector: Are other civic tech organizations relying on data in the same way, and do they promote similar ends with them? Are actors outside the field of civic tech adopting them, and in what way? Which actor in the civic tech sector influences what field? Given civic tech’s status as a ‘pioneer community’ (Hepp 2016), tackling these questions is one way to conduct empirical research to trace and understand how forms of civic engagement and activism are changing due to their growing reliance on data.

Introduction

‘Ayuda Ecuador,’ a deployment of the Ushahidi platform, was launched on April 16, 2016, after the first tremors of the earthquake that killed 660 people and injured 4,605 (USAID 2016). Within a matter of two hours, a group of ‘digital humanitarians’ – who typically comprise bloggers, techies and activists willing to volunteer their time to assist in emergencies remotely (Gutiérrez 2018a) — launched the deployment to ‘generate collectively data relevant to the emergency, threats, logistic needs and response that the affected population was experiencing (…) and effectively channel the efforts by different institutions and agencies’ (Ayuda Ecuador 2016).

The Ushahidi platform – a non-profit tech company from Kenya that allows the visualization of crowdsourced crisis data for humanitarian purposes – is a proactive data activist organization, proactively employing software and data for humanitarian assistance (Gutiérrez 2018a).

Data activism involves a series of practices ‘at the intersection of the social and the technological dimension of human action,’ aiming at either ‘resisting massive data collection,’ in the case of reactive data activists, or ‘actively pursuing the exploitation of available data for social change,’ in the case of proactive data activists (Milan and Gutiérrez 2015, 127). This article deals with the second type of data activism.

Proactive data activism is currently being harnessed to understand, analyze and develop solutions to a range of social problems, from climate change and biodiversity loss, to inequality and human rights abuses (Hogan and Roberts 2015), as well as to assist in humanitarian crises, as in the case of the Ushahidi platform.

Because of its capacity to generate alternative digital public spheres (called here APSs for short), this paper specifically refers to a type of proactive data activism termed ‘geoactivism,’ which employs interactive cartography to communicate and trigger action. In this paper, Geoactivism galvanized to address an emergency is called ‘crisis mapping,’ while geoactivism employed in evidence-gathering for advocacy is called ‘activist mapping’ (Gutiérrez 2018a).

The cases mobilized to illustrate this article include mainly deployments of the Ushahidi platform, based on crowdsourced data, as well as other cases, including InfoAmazonia, which relies on public data, as well as crowdsourcing and sensors to generate data. They are introduced in the analysis because they are typical geoactivist organizations (Gutiérrez 2018a).

To examine how APSs behave, I draw on ‘Why the net is not a public sphere’ (Dean 2003). Particularly useful are Jodi Dean’s reflections on how the net cannot be considered a public sphere, but a set of democratic configurations that she calls ‘neodemocracies’ (ibid., 105). Dean considers that the architecture of the public sphere is based on a set of components – specifically, site, goal and vehicle, as well as means and norms (2003, 96). I take this idea further to highlight facets of the APSs (see Table 1).

The primary purpose of this paper is not to compare the public sphere and Dean’s neodemocracies, but to contribute to a theory of data activism, a relatively unexplored phenomenon. Therefore, the theoretical comparison provided in this paper is a heuristic exercise for enlarging the concept of the APS in data activism.

Discussion

In this section, some of the central concepts and theories mobilized for this study are explored and put in the context of data activism, including Dean’s neodemocracies (2003), Jürgen Habermas’s public sphere and its criticism, James Bohman’s electronic public space (2004), Nancy Fraser’s counterpublics (1990), and the theory of dataveillance (Poell, Kennedy and Dijck 2015). A brief comparison of the public sphere, the neodemocracies and the APSs is offered, which is explored in the next section.

According to Dean, the Habermasian public sphere arises in the nation, is aimed at consensus, abides by the rules of inclusivity, equality, transparency and rationality, and revolves around actors (2003). Meanwhile, Dean’s internet-based neodemocracies aim at contestation, happen on the web via networked conflict occurring within the canons of duration, hegemony, decisiveness and credibility, and emerge around issues (ibid.). The concepts emerging from this taxonomy are employed here probingly to observe the APSs generated by data activism.

I do not entirely subscribe to Dean’s criticism of the Habermasian understanding of the public sphere. Dean’s criticism does not take into consideration how the concept of the public sphere has evolved in Habermas’s thinking. Habermas updated it, suggesting that ‘a cosmopolitan matrix of communication’ could make a ‘world public sphere’ possible (Habermas 1996, 514), decoupling the public sphere from the nation. Therefore, when I uphold the idea that the public sphere is linked to the nation, I am referring to Dean’s critique.

Besides, the Habermasian public sphere is not as placid as Dean portrays it, since it also deals with struggle and is intended for action. In the public sphere, discourse commences once there is a problem and actors can contest each other’s validity claims (Habermas 2006, 413). In fact, the public sphere is the place where differences are settled by reason, and not by war (Negt and Kluge 1993, ix). Likewise, when I mention consensus as the goal of the public sphere, I am using Dean’s views.

One of Dean’s criticisms is that adding an ‘s’ to the concept does not save it, because groups either share the same norms (and therefore are a public in Habermasian terms) or they do not (and therefore are not a public) (Dean 2003, 97).

This distinction does not apply easily to the APSs created by data activism. APSs may share the same norms, underlying values and solidarity, but their evanescence makes them plural and ad hoc. They are similar to Stefania Milan’s karst river movements, which remain dormant, reappearing when conditions require it (2013), while their members feel part of the same movement and principles (Grabowski 2012). They match Donatella della Porta’s and Mario Diani’s description of a network organisation combining formality (i.e. norms) with elements typical of a distributed network, based on the independence of single components which adapt and change as the occasion demands (2006, 159).

Dean’s taxonomy arranges the features of the public sphere and the neodemocracies into boxes whose boundaries blur once you look more closely at them. Conscious that any classification is arbitrary, I employ Dean’s taxonomy as a heuristic tool for reflecting on data activism.

Thus, APSs’ frame of reference is the local and the global concurrently (not the national or the web), since they are ‘transnational,’ in Bohman’s terms (2004). They are directed at action (not solely at consensus, as Dean asserts for the public sphere), and are established around conflicts (not actors or simple issues).

 

Table 1: Comparing the public sphere, neodemocracies and APSs

	Public Sphere	Neo- democracies	APSs
SITE	Nation	Web	Local/global
GOAL	Consensus (legitimization)	Contestation	Action (via consensus)
VEHICLE	Actors	Issues	Crises and problems
MEANS	Procedures (legal, national)	Networked conflict	Networked coordination (via procedures)
NORMS	Inclusivity Equality Transparency Rationality	Duration Hegemony Decisiveness Credibility	Inclusivity and evanescence Equal access Transparency Credibility

 

Source: Elaboration by the author based on Dean (2003, 108).

This new taxonomy allows me to make some observations about how APSs behave.

Other authors’ arguments, compatible or not, are employed later in this paper for thinking about APSs, although the intention is not to subscribe to any theory of the public sphere.

For example, Bohman’s reflection on ‘a new kind of public sphere’ that emerges in the era of ‘electronic democracy’ (2004, 131) is useful. Bohman acknowledges that an ‘electronic public space’ – understood as a computer-mediated, Internet-enabled communication — may have the potential of generating a ‘novel public sphere,’ enlarging the scope of ‘certain features of communicative interaction across space and time,’ especially when applied to ‘deliberative transnationalism’ (2004, 132-151).

Data activism could be considered an enhanced variety of activism in that it is enabled by a cutting-edge form of technology (i.e. the data infrastructure), and data activists often use the Internet as a platform for disseminating information (together with other information and communication technologies or ICTs). Bohman’s viewpoints are therefore interesting for considering how data activism employs technology to generate novel public sphere(s) for transnational advocacy and humanitarianism.

This article draws too on Fraser’s look at marginalized groups excluded from the public sphere, and the formation of their post-bourgeois public spheres as ‘subaltern counterpublics,’ overcoming a ‘hegemonic’ mode of domination (1990, 61-62). These groups generate counterpublics, or ‘parallel discursive arenas,’ to develop and circulate ‘counter-discourses,’ which present alternative ‘interpretations of their identities, interests and needs’ (ibid., 67).

Although there is a difference between Fraser’s bottom-up counterpublics and the spaces generated by proactive data activism, her conceptualization is useful here too. In 2008, four volunteering techies and bloggers, who could hardly be described as ‘marginalized’ in spite of being ‘repressed,’ launched the first deployment of the Ushahidi platform.

This first application was set up to bypass an information shutdown during the bloodbath that arose after the presidential elections in Kenya a year earlier, and to give voice to the anonymous victims. The deployment allowed victims to disseminate alternative narratives about the post-electoral violence. Since then, other applications have been set up by campaigners, organizations and digital humanitarians in every major disaster not only to visualize what is happening but also to assist people affected in real time, offering some ‘expectation of a response’ and opening a ‘space for interaction,’ in Bohman’s words (2004, 135). Namely, the APSs of data activism could be considered subaltern in that they are parallel discursive arenas where people disseminate counter-discourses in opposition to top-down narratives and approaches.

This paper also takes into account the backdrop in which proactive data activism emerges, which includes the datafication process – that is, computing aspects of life that had never been quantified before (Mayer-Schönberger and Cukier 2013, 78) — and the massive collection of data by governments and corporations, giving rise to ‘computational politics’ (Tufekci 2012), what Braman calls the panspectron (2009, 314) or dataveillance (Poell, Kennedy and Dijck 2015). Dataveillance employs ‘the massive flows of data circulating between devices, institutions, industries and users’ for ‘new and troubling practices’ (ibid.). Reactive data activism, a frontrunner of data activism, emerges precisely in answer to this data-gathering and intrusion (Milan and Gutiérrez 2015).

Labels employed in this paper are derived from Gutiérrez (2018a), where eleven attributes observed in forty data initiatives (i.e. how collaborative a concrete organization is), thirty semi-structured interviews with data activists, practitioners and experts, and four in-depth interviews with Ushahidi developers and deployers were the basis for a non-normative taxonomy employed in examining proactive data activism (ibid.). According to this classification, proactive data activists can be categorized as: skills transferrers, focused on transferring data or social science skills, forging opportunities for alliances and generating digital platforms and tools for data activism; ‘catalysts’ who chiefly fund data projects; activists who produce data journalism, when news media organizations are not willing or capable of doing so; and the actual data activists and geoactivists (ibid.).

The ability of some proactive data activist initiatives to generate APSs is the pivotal idea in this paper. Happening both locally and globally across borders (site), aiming at action (goal) and gathered as a temporary network around the crisis caused by the earthquake (vehicle), ‘Ayuda Ecuador’ is an example of how data activism can generate temporary spaces for dialogue and action.

The ‘deployers’ at ‘Ayuda Ecuador’ included several organizations from different countries, supported by four experts: Luis Hernando Aguilar, on knowledge management; Byron Herrera, on software development; Estela Navarrete, on mobile software development; and Angela Oduor, from Ushahidi (Ayuda Ecuador 2016). The application connected them and other digital humanitarians with victims (reporters) generating data via the crowdsourcing platform, and traditional humanitarian agencies, using the resulting information on the ground. For almost three weeks, these three communities – that is, deployers, reporters and humanitarian workers — ‘met’ in the public space generated by the platform to agree, make decisions and coordinate actions.

The theory of the public sphere is employed here to analyze these spaces. It was Habermas who described the public sphere’s emergence in the 17th and 18th centuries as space where a communicative action leads to mutual understanding (Habermas 1984, 17–18). However, Habermas was criticized for having created too abstract a model, and for having bypassed some challenges, such as the exclusion of women and men from the lower classes, and the heterogeneity of the public (Webster 2006, 163–68; Calhoun 1992).

Nevertheless, the idea of the public sphere has been revived with the emergence of ICTs, the ‘networked information economy’ (Benkler 2006, 212), and the data infrastructure — including databases, algorithms, servers and systems needed to obtain, curate, analyze and visualize data (Gutiérrez and Milan 2017) —, due to the fact that these technologies appear to augment opportunities for interaction and debate (Rheingold 2002; Papacharissi 2002; Benkler 2006). Habermas himself reviewed the concept, suggesting that ‘a world public sphere’ could become possible (1996, 514).

However, in the ‘network society’ (Castells 2010b), the public sphere does not remain intact. The rise of ‘digital contention’ generates a ‘structural transformation of the public sphere,’ where access, participation and communication ‘are constantly redefined and renegotiated’ in ‘multiple arenas‘ (Celikates 2015, 14).

This revival, again, met with more criticism. Distinctions between the bourgeois and proletarian public sphere (Negt and Kluge 1993), national and translational publics (Bohman 2004), the Habermasian public sphere and the ‘networked public sphere’ (Friedland, Hove, and Rojas 2006), as well as discussions about whether the concept of the public sphere could be applied to the net (Dean 2003) and social media (Fuchs 2013) ensued, leading the debate towards ‘opposing assessments’ (Bohman 2004, 132).

For Lewis A. Friedland, Thomas Hove and Hernando Rojas, the ‘centrality of networks’ demands revisions of the public sphere (2006, 5). These authors argue that the ‘networked public sphere’ presents challenges to the public sphere in that it raises questions about the structure of communicative action, as the institutions of socialization are now networked, transforming ‘core forms of intersubjective communication and sociation’ (ibid., 24).

Echoing Oskar Negt’s and Alexander Kluge’s proletarian public sphere, Christian Fuchs critiques ‘the contemporary web’ and social media as false emancipatory public spheres because they are dominated by capitalist interests (2013). He adopts the notion of ‘proletarian counter-public sphere’ as a ‘radically different’ public sphere (Fuchs 2010, 176).

Concurring, Dean notes that the net is ‘the architecture for communicative capitalism, both as order establishing itself and as an order being resisted’ (2003, 105). The online presence of activists supports her arguments that resistance and discord are the goals of the web (ibid.). There is a different way to look at it. The data infrastructure embeds several forms of bias, discrimination and asymmetry, which do not disappear when activists use it for their causes (Gutiérrez 2018a, 2018b). However, I do not think that these contradictions annul the APSs’ power to foster democratic participation; they simply reflect the complexity of our world, where simultaneous, often clashing, phenomena happen.

Having framed the discussion, what follows is an analysis of the comparison between the public sphere, the neodemocracies, and the APSs.

The Tensions of the Site, the Goal and the Vehicle

Data activism imposes tensions on the public sphere. The following section dwells on them, paying particular attention to three strains (site, goal and vehicle), which are mainly significant because they present the most noteworthy differences between the public sphere, the neodemocracies, and the new APSs.

The Tension of the Site

Several communities typically convene when launching a Ushahidi deployment tackling a crisis: the group of expert ‘digital humanitarians’ (Meier 2015a) setting up and supporting the deployment remotely; the people affected by the disaster or violence, who contribute their data and use the information; and the humanitarian workers, responding to the crisis.

The first group can be based on different geographic locations, taking advantage of the ‘space of flows,’ which allows real-time social practices to happen simultaneously (Castells 1992, 2010a). The second and third groups operate locally, on the ground, and globally, when they communicate in the APSs generated by the platform. ‘Ayuda Ecuador’ offers an example: ‘locals’ (i.e. on-the-ground humanitarian personnel and reporters) and experts from different parts of the world (i.e. the digital humanitarians) create a space where they can discuss and coordinate actions.

According to Dean, the public sphere occurs in the nation, while the neodemocracies are situated on the web (ibid., 108); meanwhile, the new APSs happen both locally and globally, all at once. During the Enlightenment, the public sphere surfaced in the salons of England, France and Germany, a neutral arena of critique against the absolute power of royals (Habermas 1991, 33-34). The conceptions that Habermas mobilized initially to examine the bourgeois public sphere (i.e. ‘State,’ ‘Church,’ ‘aristocracy’ and ‘royalty,’ among others) cannot be fully appreciated outside the framework of the nation.

Meanwhile, Dean states that neodemocracies – understood as democratic configurations which are organized through contestation (2003, 108) — happen on the web, a place of conflict and antagonism, where differences ‘emerge, mutate, link up into and through networks’ (ibid., 105-106).

Instead, the communities that engage in initiating an Ushahidi deployment interact in a space where the local and the global intersect via the web or other communication systems (i.e. mobile phones).

As with neodemocracies, the concept of the nation limits the way proactive data activism can be theorized. In proactive data activism, a global interaction seeks local effects. The nation can be an element in the offline aspects of proactive data activism (i.e. the level of freedom of speech of the country where the crisis happens), but it is not determining. For instance, the first Ushahidi deployment in Kenya managed to bypass national restrictions and to impose an alternative narrative thanks to a synchronized, collaborative effort.

The Tension of the Goal

APSs are created for tangible action. The interactive mapping employed by Ushahidi, for example, depicts different layers of information in diverse formats (i.e. images, graphs, text). These strata are provided by reporters and deployers (i.e. where and when ‘water purification and sanitation’ services are offered on the ‘Ayuda Ecuador’ map) so that others can use them. Namely, verified reports about where water purification and sanitation services are available, and for how long, are offered so people can act on them.

Another example is the geoactivist initiative ‘Rede InfoAmazonia,’ which offers actionable information on the quality and quantity of the Amazon river’s water and issues warnings, so the communities in the West of Pará, Brazil, can make decisions in their daily lives (Rede InfoAmazonia 2016). ‘Rede InfoAmazonia’ is based on a system of connected sensors that capture data based on physical and chemical parameters, which produces cartography, data and notifications via mobile technologies when the water indicators exceed or go below boundaries of normality (ibid.).

Ushahidi largely engages in crisis mapping, increasing the efficiency in response to humanitarian emergencies, while InfoAmazonia often deals with activist mapping, generating data and empowering communities for participating in decision-making processes. Occasionally, data activism engages in contest (which places it closer to Dean’s neodemocracies). That is, crisis mapping is the art of visualizing citizen reports on a map during a crisis to enhance humanitarianism, whereas activist mapping is more focused on galvanizing communities and gathering bodies of evidence that can be used for advocacy (Gutiérrez 2018a).

Consequently, the idea of proactive data activism should be regarded here as a broad grouping that embraces cases with multiple goals related to action, but not all of them are considered in this article.

Habermas regarded the public sphere initially as a locus of rational discussion aimed at understanding (Habermas 1991). In the public sphere ‘everybody’ gets a chance to talk and to be listened to. That is why Dean considers that the Habermasian public sphere’s goal is principally consensus; in contrast, contestation is what neodemocracies aim at (Dean 2003, 109). A democratic theory built around the notion of issue networks aiming at struggle recognizes that antagonism is what gives democracy ‘its political strength’ (ibid., 111).

Meanwhile, the APSs generated by proactive data activism exhibit a propositive teleological pragmatism, as seen in crisis and activist mapping. This makes proactive data activist initiatives ephemeral: they are created only as a response to a crisis or to an issue perceived as problematic, which they seek to highlight, elucidate and even solve. When the difficulty disappears (or the deployment fails), these initiatives tend to fade as well, in the same way as collective action sometimes remains concealed and resurfaces only when the conditions are auspicious, mimicking a karst river (Milan 2013, 137–68).

The Tension of the Vehicle

The crisis or problem that the data-activist endeavor is tackling is the vehicle, if ‘vehicle’ is to be thought as the trigger for the formation of the APS. However, actors are as significant in the APSs as in the public sphere, giving good reason why they deserve some attention here too.

As with citizens’ media, proactive data activism places people at the forefront of content production (Rodríguez 2001, 20). In ‘Rede InfoAmazonia,’ local communities generate data via sensors, contribute to their interpretation and use the resulting information to make decisions. Their data agency is enhanced when the datafication process – typically the monopoly of governments and corporations — is reversed. Thus, these communities’ ‘routine association’ with automatized data collection is transformed (Milan and Gutiérrez 2015, 130).

In the case of crisis mapping, actors include deployers, reporting victims and humanitarian workers. These three communities are fundamental. Useful crisis mapping cannot occur without credible deployers, eager reporting witnesses (reporters) and collaborating humanitarian workers. The challenge in crowdsourcing data does not come from its technical sophistication, but from the convening power of the project. For instance, hundreds of Ushahidi deployments have failed due to the absence of people submitting reports from the ground, transforming maps into ‘dead maps’ (Vota 2012). Likewise, the failure of some Ushahidi deployments to engage humanitarian forces on the ground has led to hiccups as well (Keim 2012).

Actors in APSs are significant in more ways because they can include marginalized people, making APSs close to Fraser’s counterpublics (1990, 61). For example, the Ushahidi deployment in Haiti in 2010 offered visibility and decision-making power to the victims affected by the earthquake. Another example is InfoAmazonia’s ‘Annual cycles of the indigenous peoples of the Rio Toque’ project, which crowdsources and visualizes information from indigenous people (Cardoso 2015). However, APSs can be considered counterpublics, up to a point. Even if some deployers are victims themselves, there are apparent power asymmetries between the tech-savvy deployers, building and managing the platform, and the data reporters, merely using it under predetermined conditions.

Nonetheless, actors are not the vehicle for APSs. Without the emergency or conflict, the APS do not make any sense. The vehicle for proactive data activism is, then, the crisis or problem at hand, and the nature of data activist projects is teleological.

Proactive data activism can be seen as an instance of collective action triggered by conflicts and problems, which then deploys unconventional means – specifically, the data infrastructure used critically – to deal with them.

In contrast, in the public sphere actors are the vehicle. They are conceptualized as rational beings that make coherent choices, based on reason (Habermas 1984, 89-93). As said, the public sphere has been challenged from many fronts, one of them questioning who can be an actor because the bourgeois public sphere was an elitist island for educated, propertied, white men. Its character was ‘equal’ only for the privileged few, but ‘exclusionary’ for others (Calhoun 1992, 223).

Dean states that a democratic theory built around the notion of issue networks can avoid this ‘fantasy of unity’ (2003, 107). In fact, the neodemocracies are focused on issues because the web is structured by networks formed around issues (ibid., 110-111). Although actors are ‘always embedded in networks,’ they are replaceable because networks’ configuration continuously changes as players enter or leave them (Dean 2003, 108), realizing forms of autopoiesis, ‘or network-based self-organization’ (Friedland, Hove, and Rojas 2006). In a Ushahidi deployment, for example, roles are static while people can change responsibilities or move in and out of the APS.

Looking at the element of the vehicle, APSs seem closer to neodemocracies than to the public sphere in that they are characteristically triggered by (problematic) issues and not actors. However, while any neodemocratic network can be gathered around any issue, any problem will not trigger a proactive data endeavor.

Whether an issue is catastrophic or problematic enough (or perceived as such) depends on different factors. Droughts, for example, are amongst the most deadly disasters regarding the loss of lives (Live Science 2013), but because they often occur over long periods in far-away developing countries, most droughts get a fraction of the coverage that any hurricane receives in rich countries (Tzvetkova 2017). Meanwhile, public attention, media reach and emotion are essential in any successful Ushahidi deployment, since they require a high degree of buy-in among actors.

Other Tensions

The new APSs stand in contrast to both the bourgeois public sphere and Dean’s neodemocracies; but the lines which separate them become more imprecise when looking at their means and norms.

The means mobilized by APSs is networked coordination, while the public sphere is only possible through procedures, and neodemocracies ‘open up opportunities for (networked) conflict’ (Dean 2003, 108).

Networked coordination works within the APS through the formation of temporary networks for action, triggered by the conflict or problem and glued together by solidarity and values, enacting a ‘logic of aggregation’ as they assemble individuals from diverse backgrounds (Juris 2012). While the deployment lasts, the deployers become digital humanitarians, a collective category that groups people of different backgrounds, ranks and nationalities.

However, the ‘diversity of individualized actors’ imposes stress on the sustainability of the effort (Juris 2012, 261). In fact, the need for an unremitting exertion inflicts a strain within the APSs of crisis mapping. Volunteer burnout is, for example, a problem in humanitarian emergencies, since they can last for months, demanding a sustained determination and long hours. It is not the individual that guarantees results but the network, which is resilient because their elements are disposable. In that way, data activist organizations resemble the flexible networks described by della Porta and Diani (2006).

From the perspective of norms, the APSs are ruled by inclusivity and evanescence, equal access, transparency and credibility. This places them halfway between the public sphere, whose rules are inclusivity, equality, transparency and rationality, and the neodemocracies, governed by duration, hegemony, decisiveness and credibility (Dean 2003, 108).

Where the public sphere is regulated by ‘inclusivity’ in Habermasian terms, the neodemocracies value duration (Dean 2003, 109). But for APSs duration makes no sense, since they exist only while the conflict or problem persists. APSs get constantly activated and discontinued, and a conditioned ‘inclusivity’ is essential for generating the crowds, networks and communities that keep them alive.

Paradoxically, ‘inclusion’ in crisis mapping is delimited by the rules determining that actors must belong to one of the three communities necessary to launch a deployment. But for Bohman, ‘any social exclusion undermines the existence of a public sphere’ (2004, 134). To think about the inclusion of the APSs, this concept must be qualified.

The APSs are inclusive, but they establish boundaries for inclusion. Ushahidi deployments create spaces for equal participation with concrete rules: anybody can become a digital humanitarian, but they need to register, declare their abilities and potential contributions to the crisis, and then follow guidelines to produce as much verified, actionable information as quickly as possible. These guidelines are not imposed from external actors in a higher hierarchical position, though, but as a result of internal, horizontal processes of evaluation (Gutiérrez 2018a). For the duration of the deployment, the space is open to experts and non-experts, as long as they share the same objectives, can contribute time and comply with the rules. Then, it shuts down.

Volunteering experts are privileged people with access to technology; however, the APSs also include victims and vulnerable people. The only barrier to the victims’ mediated participation is their access to ICTs, especially mobile phones, which in the past few years has been growing [e.g. in 2008, with a population of over 39 million people, Kenya had experienced a sharp growth in mobile technology penetration, with more than 16 million subscribers (Oteri, Kibet, and Ndung’u 2015)]. In the case of InfoAmazonia’s ‘Annual cycles of the indigenous peoples of the Rio Toque’ project, participation is even more mediated.

Therefore, APSs are ruled by impermanence and qualified inclusion.

In the context of antagonism, ‘neodemocratic politics are struggles for hegemony’ (ibid. 110), and the public sphere is governed by the norm of equality. Meanwhile, the APSs are ruled by equal access.

In Ushahidi deployments, for instance, all actors can participate in the decision-making process: everyone has a parallel capacity of generating and accessing information.

However, equal access does not mean equality here. There are three primary power asymmetries that can be observed in crisis mapping. First, by verifying the information reported by witnesses, deployers turn into gatekeepers of what is public in an APS. Namely, they are placed in a higher echelon. Second, although victims can include deployers, being a deployer is not as dangerous as being a witness or a conventional humanitarian worker, and there is an abyss of difference between working on a disaster remotely and confronting the physical conditions of an emergency on the ground. Third, the reports that are published by the deployment can expose, and have exposed, the victims’ and the human rights defenders’ whereabouts, activities and networks, endangering them in situations of repression and violence (Hankey and Clunaigh 2013; Slater 2016).

Another rule of the public sphere is transparency. But according to Dean, nowadays power is not hidden, so transparency does not need to be a goal. Echoing the concept of infoxication (Toffler 1984), Dean means here that, in the net, there is not a lack of information, but the opposite: an overload. ‘Fully aware that there is always more information and that this availability is ultimately depoliticizing, neodemocratic politics prioritizes decisiveness’ (ibid.).

But contrary to what Dean says, information is not always available, even when it is most needed such as in the case of disasters or emergencies. Snowden revelations in 2013 put an end to any illusions of transparency on the part of governments too (Castells 2015).

APSs seek transparency when information is hidden, not as a final goal, but as a tool for action. Ushahidi’s first deployment was put in place precisely to break the news blockade by the Kenyan government and media.

However, too much transparency can render victims vulnerable to being targeted. The use of the data infrastructure enables digital humanitarianism, but it also produces new risks for both humanitarian workers and witnesses, since their reports can disclose their location, actions and networks to the dataveillance machinery (Hankey and Clunaigh 2013, 536; Stottlemyre and Stottlemyre 2012, 12). Namely, data and metadata produced in data activism can be harvested by people in power to control and locate activists and witnesses (Burns 2014). That is why Ushahidi deployments do not usually open their data.

Proactive data activism can also offer alternative narratives when the mainstream versions of reality are manipulated. An example is the ‘counter-mapping’ project set up by several communities in Indonesia in resistance to governmental initiatives to remap land and surrender it to companies (Radjawali and Pye 2015). Indigenous counter-mapping challenges the state power over maps, and its categorization of land uses (ibid., 4). This movement is currently testing ‘grassroots UAVs’ (Unmanned Aerial Vehicles) to generate data, and strengthen people’s datasets and political actions (Meier 2015b). Again, the idea in data activism is not to produce transparency for transparency’s sake or to create awareness, but is aimed at action.

Finally, issue networks are ruled by credibility over rationality; in contrast, rationality governs the public sphere (Dean 2003). The APSs look more like neodemocratic networks since credibility and trust are vital to sustaining the crowd and the activist effort (Gutiérrez 2018a).

The APSs are not ideal havens of cooperation and equality: power asymmetries exist within actors, and some glitches and oversights in real-life cases have created ‘dead Ushahidi maps.’ However, the rules of the APSs imply that they have to be guided by: conditioned inclusivity and temporality, within a set of procedures; equal access, so actors can communicate and act; transparency, so unconventional data-based narratives are produced; and credibility, so buy-in and adhesion on the part of the actors involved in tackling a crisis are generated.

Framing the New APSs

Based on the observations made earlier, this section justifies why the sphere of communication and action created by data activism are called here ‘alternative digital public spheres,’ and also provides a broader context for the rise of data activism in the big data society.

The APSs are ‘alternative’ because they engage alternative actors (i.e. non-experts, victims and vulnerable people) in producing counter-narratives, using technologies in unconventional ways (i.e. the data infrastructure employed critically), and sometimes altering power dynamics by reversing the monopoly of maps and the datafication process. Namely, they propose formats, actors and subjects outside the established norms.

They are ‘digital’ because they are enabled and constrained by the data infrastructure, that is, a digital infrastructure that allows the managing, sharing, storage and use of data. That is, they are digital because they are enabled by information, communication and data technologies.

The APSs conform to the rule of ‘publicness’ as a condition of the public sphere, defined by Bohman, although he talks about face-to-face interaction (2006, 134). In the APSs, there is little physical contact, but the ‘conversation’ is public not purely because it could be seen online by others but to the extent that it could be taken to address anyone, paraphrasing Bohman, (ibid.). That is, they are public because they are ‘the result of the social activities made by individuals sharing symbolic representations and common emotions in publicness’ (Mateus 2011, 275).

Data activism happens in the context of other social uses of the data infrastructure. It can be employed in two opposing ways: it can facilitate governmental and corporate intrusion, manipulation and control, but it can also be a powerful tool for interaction, activism and social change.

The unyielding datafication of all human activity has profound impacts. With big data and computational techniques, massive surveillance and manufacturing consent is easier: it has moved from the ‘magnifying glasses and baseball bats’ of the 20th century into ‘telescopes, microscopes and scalpels’ of the 21st century (Tufekci 2012, 6).

To be exact, employed as a scalpel, data infrastructure allows manipulators to engage people on an individual basis. Before data infrastructure, social scientists analyzed relevant samples of the universe they wanted to study and relied on surveys in which people said what they thought they did. With data infrastructure, social scientists now can examine what people do (Stephens-Davidowitz 2017). Data infrastructure enhances and quickens surveillance, makes it personal and renders it ubiquitous and surgical. Surveillance today involves ‘the ability to go beyond what is offered to the unaided senses and minds or what is voluntarily reported’ (Ball, Haggerty, and Lyon 2012, 26).

But the ICTs and, lately, data have transformed activism too. The rise of the data infrastructure and massive surveillance prompted ‘reactive data activism,’ which employs the data infrastructure to shelter online interactions and communities from massive surveillance (Milan and Gutiérrez 2015, 127). With reactive data activism, the infrastructure that makes it possible has become more accessible to the average user, ‘rapidly evolving from a peripheral, elitist form of activism to a diffused one, whereby also non-skilled users take part in the game’ (ibid.).

Data infrastructure allows a zooming in and out of processes and places and can offer both the high-resolution landscape of a situation and a vision of the life of one single person at a given time (i.e. a request for help posted at ‘Ayuda Ecuador’). These capabilities are incorporated into the new APSs.

The realization of the APSs is not free of contradictions. Differing from that which reactive data activists usually do, proactive data activists often resort to capitalist technologies, whose values they do not seem to share. Some of these technologies are based on business models that require the low production costs that only semi-slave conditions and child labor afford (Amnesty International 2016, 29), and create environmental damage (Hogan and Roberts 2015). Proactive data activists typically turn to whatever tool is at hand – including Google Maps, mobile technology and other corporate inventions — to respond to a crisis (Gutiérrez 2018a). Other problematic issues include the power asymmetries and the failures that real cases of data activism accumulate.

Conclusions

The spaces for debate, consensus and networked action that are generated in proactive data activism can be theorized as public spheres, because they share some of their attributes: although proactive data activism’s final aim is action, it is articulated by actors who interact and organize following procedures framed by ‘inclusivity,’ equal access and transparency. However, they do not resemble the bourgeois public sphere entirely. I have called them ‘alternative digital public spheres’ or APSs for short. Table 2 summarizes their qualities, which emerge in comparison with the public sphere and the neodemocracies. 

SITE: Local/global	Global interaction in the space of flows with local effects (local dimension also in on-the-ground action).
GOAL: Action	Ø  Consensus (which embeds an expectation of response) is a tool for action; Ø  crisis mapping’s aim is tackling emergencies and activist mapping’s is to enhance advocacy and empower communities.
VEHICLE: Crises and problems	Ø  APSs can bypass information blockades, generate alternative narratives and counter-maps, and revert datafication; Ø  emergencies can trigger crisis mapping and social problems, activist mapping; Ø  although not the vehicle, actors are important, citizens are placed at the forefront of production, marginalized people can get empowered and some roles are fundamental in crisis mapping.
MEANS: Networked coordination	Ø  APSs behave like decentralized network organizations, where roles are static (i.e. crisis mapping), while concrete individuals are expendable; Ø  a sustained effort over time strains the endeavor, but networks are resilient.
NORMS: Inclusivity, evanescence, equal access, transparency, credibility	Ø  Norms come from the network, not imposed in top-down processes; Ø  APSs emerge with an built-in obsolescence; Ø  they are ruled by conditioned inclusivity; Ø  their equal access (not equality) is framed by asymmetries (in the roles and rules, in the level of danger and because victims can be exposed); Ø  transparency is a tool (like consensus), which can generate disruptions, but too much transparency can be dangerous for victims/reporters; Ø  without credibility, data activist initiatives fail.

Table 2: The alternative digital public spheres. Elaboration by the author.

What follows is an explanation of Table 2. APSs generated by data activism have lost the nation as a reference because they are located both in the local and the global, taking advantage of the space of flows. The action coordinated globally in the APSs has local consequences. In crisis mapping, for instance, the local determines the conditions in which victims can report, and humanitarian workers can use the information on the ground.

Actors in the APSs seek consensus, but this accord embeds an expectation of a response and is used as an instrument for action. The two main varieties of geoactivism, crisis mapping and activist mapping, are targeted at action, although the former’s aim is tackling emergencies (short-termed action,) and the latter is deployed to generate evidence for advocacy and empower communities (medium- and long-termed action). In APSs there is a difference between the local and the national. For example, disasters may affect vulnerable people in remote rural areas more than others living in cities of the same country. Also, catastrophes do not respect national frontiers.

APSs are formed around conflicts and problems they seek to address, and sometimes they bypass information blockades and generate alternative narratives and counter-discourses. They can allow ordinary people to generate their own data on their own terms, reversing the datafication process, which is typically the monopoly of governments and corporations. Emergencies can trigger crisis mapping, which is the geolocation of citizen data to assist humanitarian efforts, while activist mapping can be activated by social causes. Although not the vehicle, actors are essential in APSs: citizens are placed at the forefront of content production; disenfranchised people are at the decision-making table, which empowers them and enhances their agency; and some roles are fundamental in crisis mapping, for example.

APSs are managed by decentralized, networked coordination in which roles are static, while individuals are expendable. Although a sustained effort over time can impose strains on the actors supporting the APSs, it can be overcome by the resilience of the network.

The norms that rule APSs come from the network, that is, they are not imposed in top-down processes by external actors. APSs in data-activist initiatives are fluid and short-lived, embedding a built-in obsolescence because they arise in the face of finite emergencies and problems. Another rule governing APSs is conditioned inclusivity, which allows the participation of anyone, as long as he or she follows the guidelines, for example, to produce verified reports during crises. However, this equal access (not equality) embeds some asymmetries. In the case of crisis mapping, for example, deployers act as gatekeepers of verified reports, victims and humanitarian workers often face dangerous situations, and victims can be exposed by the APS. Transparency, another rule, is employed in the APS as a tool and can generate disruptions when it becomes evident that, for example, news media organizations are not doing their jobs. However, too much transparency can be dangerous for victims reporting data about their situations. Finally, credibility, needed to generate buy-in, ensures the success of the data-activist initiative.

But exactly as is the public sphere, the APSs are a construct. They are offered here as a heuristic concept for understanding how proactive data activism raises networked communities of individuals from a diverse background, sometimes based in remote locations, for coordinated action. APSs, in which actors work in perfect harmony towards results, are never to be found in real life, as actual-life implementations discussed in this paper demonstrate.

What real cases show is that, with enhanced data-based capabilities, activism can unleash processes of real communication conducive to action. APSs also provide another turn of the screw in the theoretical debate about the validity of the public sphere as a concept today.

Introduction

Data are often framed as tools of organized, entrenched power. The etymology of the word “statistics” reveals its origins as an aspect of the operation of the state, and it has traditionally been states, bureaucracies, and large capitalist enterprises that accumulate and manage through data, and especially through digital information-processing technologies. Indeed, since the 1960s, the dominant models of organizational action have been information-processing models (Simon 1997). Data, then, have largely lived within these centers of power and indeed have been thought of largely as consolidating that power.

To some extent, though, recent shifts have challenged this status quo. While most data sets remain located exactly where they were—in centers that have the capacity to produce, analyze, and make use of data—technical developments, such as the burgeoning capacity for information processing in the hands of everyday users, and social developments, such as transparency initiatives and movements advocating for more public access to data and data literacy, have begun to put similar kinds of big data and data processing tools in the hands of individual citizens, grassroots organizations, activist groups, and others engaged in what we might broadly class as “data activism.” This offers the potential for new forms of activism and a reconfiguration of power relations – the opportunity for forms of “sousveillance” to counter the “surveillance” at the heart of data-driven governance, for example.

We ask, what happens when these new opportunities must be enacted in the richer textures of practical organizations, and how do actors and activists resolve the tensions and frictions that shape the contexts of their action? In this paper, we examine the practical realities of everyday data activism in urban governance, with a particular focus on the ways in which people balance the activist ideals and institutional mechanisms of their daily work. In so doing, we seek to unpack notions of “activism” itself and identify within it multiple different moments, functions, and positions individuals may adopt for achieving activist goals and for opening up opportunities for others. Our argument draws on material from an ongoing research investigation into civic data work. For the last ten months, the first author has been conducting ethnographic fieldwork with an official urban data team. This team sits in the office of the mayor of one of the largest cities on the West coast of the United States. Our ongoing study looks at the practical, day-to-day work of the data team, including advocacy work (articulating and communicating the value of open data and extending data capabilities, both internally to departments and externally to the public), organizational work (including formulating, running, and publicizing specific data-driven initiatives, and developing and maintaining collaborative relationships with other parts of city government), and technical work (working with the city’s legacy systems, and cleaning, regularizing, publishing, and maintaining data sets). Through participant observation, it becomes apparent that many of the grand notions surrounding the revolutionary power of big data for both social justice aims, or for the reinvention of the bureaucracy, occur incrementally, collectively, and non-linearly, in the quotidian moments of data work. Here, the ambivalences and contested dynamics of fields of activist action become more prominent.

The data team itself comprises a range of diversely skilled individuals who evince (in casual conversations, presentations, and formal mission statements alike) a strong individual and collective concern with open government and the value of data. They see open access to data as a site for accessible and equitable city services, enhanced quality of life for all citizens, and deeper forms of citizen engagement across the range of city government functions. Some members of the data team consider themselves data activists, or at the very least more on the “radical” side of the civil servant ethos, as stated by the chief data officer in conversation with a social media platform research team in the planning of a forthcoming partnership. The actors leverage a variety of resources—such as county and state, or private, sources of data, as in the case of this collaboration—to achieve these “open” aims even as they work inside city government and within its practical limits.

Drawing on Meyerson and Scully’s notion of “tempered radicals” (Meyerson and Scully 1995), we consider how the staff of municipal data offices, and other informal data workers, enact their own “more radical” agendas and forms of data activism while negotiating the complex dynamics and organizational structures of city governments. This formulation encourages us to adopt a more nuanced approach to questions of power, activism, and resistance, one that focuses on the variety of configurations of “insider” and “outsider” perspectives, the complexities of meshing politics and practical action, the diversity of positions within the landscape of resistance, and the ongoing daily encounter with the ambiguities of positionality. Our ethnographic work, then, provides a starting-point for a broader reconsideration of how politics and activism are enacted in practice.

Data Activism, Organizing, and Ambivalence

Exploring the political consequences of the contemporary spread of data-driven analytics, Milan and van der Velden (2016) describe data activism efforts as supporting “the emergence of novel epistemic cultures within the realm of civil society, making sense of data as a way of knowing the world and turning it into a point of intervention and generation of data countercultures.” They suggest that data activism might even incite new populations of tech activists, stating:

because datafication is such a prominent feature in public life, data activism, as a mode of dealing with it, might progressively appeal to more diverse communities of concerned citizens, beyond the expert niche of previous incarnations of tech activist engagement (Milan and van der Velden 2016, 4).

Milan and van der Velden describe the opportunity for activists to appropriate the technical tools of data analytics typically associated with sites of corporate power and municipal governance, and the opportunities that those tools provide both for holding such entities to account and for offering alternative accounts of matters of concern. Central to the authors’ concern is a notion of resistance, counter-hegemony, or alterity – that is, the idea that the core of any activist position is its opposition to mainstream thought or to traditional power.

However, as writers such as Turner (2006, 2013), amongst others, have noted, stark dividing lines and oppositional stances are not always so clear. Particularly in the domain of digital technology and its development, a complex interplay can be seen between mainstream culture and counter-culture “resistance.” This interplay results in complicated, hybrid contexts, such as former Merry Prankster Stewart Brand operating the camera for Douglas Englebart’s famous demonstration of his military-funded NLS project. Turner traces a complex lineage, reaching back to American anti-fascist movements, in which the distinctions between “conventional” and “counter-cultural” positions are not always easy to discern.

Noting that “politics always takes place in a field criss-crossed by antagonisms,” Chantal Mouffe (2013) has advanced a political philosophy focused on disagreements, local tensions, and radical plurality. Perhaps most important for us here is the intricate texture of the forms of agonistic political relations that she explores, in which, again, no global orientations are easily sustainable in the face of a multi-dimensional and multi-faceted series of local tensions and distinctions of position. In light of Mouffe’s thinking, any activism must always be read locally, as a relational matter, and as a matter of degree, allowing again for a complex internal polyvocality. Sometimes, this polyvocality reflects intersectional concerns and the work involved in engaging in, and maintaining positions of, strategic essentialism in order to achieve particular political ends. Sometimes it speaks purely to the pragmatics of effecting action.

In a recent episode of the Politically Reactive podcast series (Bell and Kondabolu 2017), guest Patrisse Kahn-Cullors—one of the founders of the Black Lives Matter movement, and a decorated social organizer—spoke briefly on her role as an organizer. When one of the hosts queried why she identifies as an organizer instead of an activist, Kahn-Cullors answered that she works to make distinctions between the various critical roles in resistance movements, namely the activist and the organizer. The activist, to paraphrase, is the one who goes out and does the action, one such example being attending a protest. Conversely, the organizer lays the groundwork for there to be a protest in the first place, through planning, organizing, and networking. Kahn-Cullors states, that for her, activists are “super important” as “they sign the petition, they show up to the rally, they show up to the board meeting. But the organizer is the one who develops the petition, they are the ones who get the rally together, the ones that bring communities together so that activists can show up.” Both roles are critical in order for social change to take place.

Within the revolutionary rhetoric of the digital, these pragmatic considerations and organizing efforts often go unrecognized. Tufekci (2017) draws attention to this oversight when discussing the role of digital technologies in supporting and organizing protest movements in sites such as Istanbul’s Gezi Park and Cairo’s Tahrir Square. While celebrating the power of social media to mobilize and connect large numbers of people in the absence of prior large-scale social infrastructure, she cautions that traditional protest movements build infrastructure slowly as they grow, and so are more likely, when decisive moments come, to already have in place the support structures necessary to, say, provide food, clothing, material assistance, and medical care to protesters. In enabling mass assembly without passing through the phase of building a community, movements grounded in social media risk focusing on the “activism” component of Kahn-Cullors’ construction without proper attention to the “organizer” role.

Building on these analyses, we want to take a further step, moving from the organizational requirements of activism towards a consideration of the potential configurations of activism in organizations more broadly. We see “activism” here as a complex set of practices and acts of positionality, encompassing many different kinds of work. Importantly, we aim to underscore a need to look at activism and social change at the scale of the micro negotiations of the everyday, while understanding their affects are neither discrete nor direct. Our field site is one where the data team members find themselves navigating and leveraging the uncertainty in large formal institutional structures undergoing change, working to effect social change, but charged too with the need to acknowledge limits and conditions upon that change. The questions that this paper attempts to open up is just how radicalism is “tempered,” in Meyerson and Scully’s (1995) terms, and how that shapes encounters with new understandings of data, particularly in terms of what new forms of data and data technologies can mean for social justice and urban governance.

The budding forms of data collection and expression that we see in the data team (making data open, automated, and interoperable), provide new ways of knowing, or new knowledge, and along with them alternative methods for challenging hegemonies. We approach activism in this domain as a collective process. In reflecting on our own experiences at the study field site, we ask: how might those with more radical aims incorporate activist modes and ideals into the everyday operations of a large urban bureaucracy? What does it mean to do so at a politically volatile time? We focus on data activism as it is expressed in organizing roles, embodied by the ambivalent data activist. Here, we consider the productive ambivalence in everyday data work for understanding how uncertainty is leveraged to push activist ideals in a bureaucratic setting. The characteristics of this setting, along with the motivations of the individuals who inhabit it, allow for expressions of activist ideals and of “making sense of data as a way of knowing the world and turning it into a point of intervention” (Milan and van der Velden 2016, 1).

Possibilities in Being Data-driven

The broader domain of this inquiry is the increasing turn to data-driven approaches in urban governance. Promising greater transparency, quality of life, and civic participation, these data-driven approaches avow to more effectively and efficiently address complex social problems. While information objects have long been a key resource in the creation of systems, products, and policies that shape the world in which we live, this current movement—what many term a “data revolution”—marks the advent of a new and forceful volume, variety, and velocity of data (Kitchin 2014). Derived from sources such as citywide sensor systems and more pervasive online tracking through a myriad of personal devices, these new data assets engender new forms of data curation and processing, along with a greater anticipation of new possibilities.

Milan and van der Velden characterize the big data movement writ large as evoking “a broad set of socio-technical phenomena enveloped in quasi-mythological narratives that univocally emphasize possibility and magnitude” (Milan and van der Velden 2016, 2). The “datification” of everything, from sleep habits to stock-brokering to municipal sanitation services, emphasizes in particular the possibilities of better, faster, and more accurate decision making; these “better decisions” become the foundation for a more effective problem solving. This is owed in part to the seeming objectivity and representativeness located in big data’s near constant accumulation and administering, as compared to earlier forms of quantification. This data-as-truth account is leveraged by data activists and the civic realm (citizens, civic technologists, and city staff) alike; each hope for data to more objectively expose entrenched injustices and enable equitable futures.

The movement is not without its critics. Many are quick to point out that these new forms of data-driven decision-making may—much like extant forms of social rationalization—end up reinforcing both existing inequities and hegemonic interests (Kitchin 2014). There are certainly concerns that these novel tools will make and remake unjust futures and do so with greater potency. But while many have viewed the burgeoning enthusiasm around the possibilities of big data with skepticism, there exists a greater lot of researchers, corporations, technologists, and other technology users and data workers who encourage and normalize the potentiality of big data. Such stakeholders have lent a legitimacy to this burgeoning movement, as exhibited by the increasing number of formal data positions and identities (such as the data scientist, the data janitor, the chief data officer, the data journalist, official city data teams, and so on); in fact, a current project of this urban data team is to expand the formal position titles and training tracks within the bureaucracy to “modernize the workforce,” encompassing more of such roles and skills.

Context: Everyday Data Work

The data workers in our study sit in the mayor’s office in one of the largest cities in the United States, with a population of well over one million and a city government that employs about 40,000 people. Established in 2013—when many cities around the world were beginning to “open” data— this team is responsible for managing data assets and related technologies within and beyond city hall, for making data visualizations and data dashboarding tools available to departments and citizens, and, most expressly, for motivating the city to be open and interoperable with their data practices for the making of data-driven decisions. This latter charge involves encouraging different departments, initiatives, and teams to work across otherwise siloed operations in order to utilize each other’s data, as well as other sources of public (county) and private data (via data warehousing services and tech companies). The aim here is to optimize data operations and engender a level of transparency whereby departments and citizens alike know how (and what) the city is performing.

The team itself is comprised of a small but diversely skilled set of data enthusiasts who collect, clean, analyze, visualize, package, and promote data assets, techniques, and tools; much of this work occurs behind the face of a given initiative (e.g. recently the team cleaned, visualized, and trained a group of city staffers working on domestic violence awareness and prevention). The professional and educational backgrounds of these individuals run the gamut, spanning from software engineering and business analytics, to public policy and communications; together these skillsets forge the necessary work of accumulating, organizing, and communicating the city’s data. Whether it is a focus on equity, transparency, or environmental sustainability, each team member is invested in data-driven governance as a means to enhance the quality of life for city staff and the citizens they work for, to make the city more “livable” for all.

The data with which they work are generated from a range of government functions, including city services (e.g. tracking speed and distribution of graffiti cleanup or waste management), urban planning (e.g. measuring traffic, tracking affordable housing stock or the quality of paved lane-miles and sidewalks), and economic development (e.g. understanding local business needs, or the landscape of employment opportunities in the growing tech industry). In the best cases, this means that data from numeric tables in static spreadsheets will be moved to a system (via sensing or scraping) that automatically collects and updates standardized data recurrently, whereby the data sources have defined “owners” and all necessary metadata, and in some cases universal IDs (categories that work across all operations in the city).

Opening and activating new streams of data, then—especially all-encompassing, continuously accumulating data—requires a lot of organizing. Organizing toward transparency can be a challenge in a bureaucratic institution for a variety of reasons, not least of which is the fact that making governing processes transparent creates new accountabilities that departments may not yet have the bandwidth to address. While there was indeed an executive mandate to open data and become both more open and more data driven, there is little direction on how this should work in practice, or how to define success. Thus, there is much uncertainty surrounding the open data movement at the sites of actual instantiation, especially with regard to authority, accountability, and even the very definition of data work.

Uncertainty, Bureaucracy, and a Tempered Approach

A bureaucratic setting may not be the first that comes to mind when looking for examples of data (or any form of) activism or radical thinking. However, through leveraging contingency and ambiguity in day-to-day workflows, and by embodying the position of the “tempered radical” within those areas whereby a given bureaucratic staff member maintains both an authentic activist self as well as a professional position self, opportunities to exercise activist aims arise.

In The Bureaucratic Phenomenon, Michael Crozier (1964) outlines a theory of power and uncertainty, particularly the important role of the latter in enabling agency as well as stabilizing conflict (if one is to bend rules or exploit uncertainty, it is important that organizational homeostasis remains a possibility). Crozier offers a broad study of a French monopoly where automation is not able to fully rationalize work or action, and he characterizes the ways in which there remain undefinable, human-dependent actions within an organizational system. These un-rationalized moments create a wellspring of wiggle room against authority.

Crozier finds that “even those with the least amount of status in an organization” will push “to the extreme their initial advantage—their control over the last source of uncertainty remaining in a completely routinized organizational system” (Crozier 1964, 154). The reason uncertainty becomes important is because it removes predictability. Where behavior is determined, it will be obvious and anticipated. With full rationalization, there would be little need for interpersonal negotiation, leaving no room for bargaining or ingratiating, no room to expand or push upon the boundaries of roles or duties. Crozier argues that in a completely determined and predictable bureaucracy, power relationships could not develop because staff would exist in a context where no one person’s actions would have any potential to impact the behavior of another’s.

In being a sanctioned office of the mayor, this data team certainly owns some leverage for making data demands of other departments. However, there are many ideological and infrastructural constraints to fostering engagement and transparency through open data. Due in part to this being both a newer office, concept, and indeed a new organizational function, there is considerable uncertainty as to what these data projects produce, or how they should be structured. The data team attempts to take advantage of this lack of determinacy in defining their roles in relation to data as a means of procuring it from less than willing departments, (those who, perhaps, do not see the value in increasing their workload to clean and “open” their data, or do not have the capacity to do so, nor the capacity to address what the data might reveal). However, these departmental operations are already rather opaque, and departments are likewise able to contest or expand upon the areas of uncertainty in turn, offering up reasons of bandwidth, capability, political, or infrastructural complications that prevent them from submitting to the data team’s requests. While many leverage this uncertainty against open data advocacy (in the very form of resistance against authority Crozier hints at), ambiguity can be operationalized in favor of activist-leaning goals with the new tools of big data.

Although full rationalization and prediction is often the goal of big data, it is still a novel and unfolding practice, especially in these civic spaces, and the data team can and does define the meaning and structure of this data work, iteratively and collectively, in a variety of creative manners, to expand their own domain of authority. This may translate into a series of more informal programs, meetings, and trainings to build mutual understanding in micro-moments and discussions over time, or it may mean sacrificing the visibility of their own work in order to build bridges and partnerships on matters that they care about. It may also mean leveraging external networks such as the neighborhood, academic, and civic tech communities—in the form of events, public talks, trainings, or dedicated projects. In leveraging others’ time, motivation, and expertise, the team is able to push projects forward that may otherwise raise hackles inside the bureaucracy, or simply become stalled due to lack of proper economic or personnel capacity.

In contrast, the overarching structure of bureaucratic rules and hierarchy create just enough unity to avoid conflict; organizations are buttressed by the fact that employees need to coexist, which often includes a mutual dependency, and a mutual desire for efficiency, ease, or efficacy in getting the work done. In the context of urban governance, the mayor’s influence, in tandem with an overall desire for recognition, may become a structuring order, but as each department holds domain over their given expertise, or in this example, their own information and data, departments and individuals within them can exploit the unpredictable variables of their territory to strike a balance.

Crozier also shows the tendency, with the advent of new technologies, to move toward “technical controls instead of human leadership.” This desire to displace the human is rhetorically framed as a move away from the “affective” and mistake-ridden to the “rational” action of machines, and such a logic can be found again in the big data or the data-driven governance paradigm. In this movement, there is perhaps an aim to rationalize even more invasively, and more rapidly. This is in some ways the entire premise of data-driven governance: a move not only to rationalize but also to automate (and predict) decision-making so that action is based on information unsullied by human affect. How can these same rationalizing techniques instead be leveraged for a more positive and inclusive form of social change?

Exploiting Uncertainty: Data Activism as Tempered Radicalism

Milan and van der Velden (2016, 7) note that “[m]any contentious actions in data activism are performed at the individual level: think, for example, of engaging in programming or inserting data into a spreadsheet.” Bureaucratic employees similarly—especially in zones of uncertainty—have the agency to exploit ambiguity in their discrete, daily work practices and “perform activism at the individual level” even in the rote aspects of data work. The concept of the “tempered radical” becomes important here, which is a theoretical and feminist position of productive ambivalence (Meyerson and Scully 1995).

Tempered radicals are individuals, often located in a professional organization, “who identify with and are committed to their organizations, and are also committed to a cause, community, or ideology that is fundamentally different from, and possibly at odds with the dominant culture of their organization” (Meyerson and Scully 1995, 585). Frequently, these are people whose work focuses on aspects of organizational change or evolution. While this mode of boundary-pushing and change is situated in the domain of organization science, their concept is in conversation with both feminist and activist inquiry. Here the authors seek to expand the frame of “being radical” most commonly featured in literature, and move toward a “collaborative division of labor among activists” which will help to buffer against what they term “the counterproductive tendency […] to judge who is being the best and most true advocate for change.”

Adhering to radical ideals, while working professionally in a bureaucracy of slow and opaque data practices, is one such example. Importantly, the tempered radical is a position of purposeful ambivalence, and it is a challenging role to occupy as it requires holding multiple intensities. The authors of the concept state that:

individuals can remain ambivalent and quite clear about their attachments and identities. In contrast to compromise, ambivalence involves pure expression of both sides of a dualism; compromise seeks a middle ground which may lose the flavor of both sides (Meyerson and Scully 1995, 588).

Far from the typically negative connotations of the word ambivalence, Meyerson and Scully show both the challenges and the constructive nature of maintaining a dualism. Simply by existing in an organization while not fitting into the organization’s overarching, hegemonic structures gives rise to owning ambivalence; this certainly holds true for the data activist enlisted in bureaucratic processes, but may also characterize anyone working through or with data who maintains skepticism about its promises. Alongside the ambivalence exists a complimentary embracing of uncertainty, or an embracing of the inability to completely control. While this may fly in the face of what using data-driven practices hope to achieve, the pragmatic response to precariousness can be used productively in the spaces of big data administering.

Strategies of the Tempered Radical

Meyerson and Scully describe four important strategies of effecting, affecting, and surviving via the role of tempered radical. The first strategy employs Karl Weick’s concept of small wins whereby the tempered radical can undertake smaller or trial projects to encourage larger moments of change, and test or expand existing and entrenched cultural boundaries (Weick 1984). Local boundary-pushing schemes can build, creating future opportunities and ultimately turning into larger projects over time. As does the human body in reaction to something perceived as invasive, so too does the organization when faced with dangerously different and potentially destabilizing ideas. A small wins approach helps to avoid any such reaction while still effecting real change.

The second strategy may seem reductive, but it is that of essentially being true to oneself, or what the authors call authentic action. Specifically, they refer to being oneself continuously and spontaneously. Being true to oneself when one does not neatly fit into predetermined parameters of organizational culture creates opportunities, through exposure, for new ways of doing things by providing examples of alternative modes. The third strategy is that of language styles, or speaking or being fluent in multiple languages (i.e. the languages of each facet of the ambivalent self, or the language of each space the tempered radical inhabits). The important work of this language strategy involves communicating a collage of these languages (i.e. knitting the radical or activist language together with that of the urban bureaucrat so as to be legible to both domains) instead of employing only one language across domains, or only the local language. Strategically leveraging language styles allows for discourse deconstruction, where the tempered radical can dismantle hegemonic ideas and assumptions in either domain to “make room for alternative conceptions of organizing and management” (Meyerson and Scully 1995, 597). Lastly, owing to the cumbersome nature of juggling these dualities, a final and important tempered radical approach involves actively garnering support. The authors cite an affiliations strategy—particularly maintaining affiliations outside of the organization for emotional, ideological, and informational support—as a means to express rather than suppress emotions and ideas, and avoid burnout.

With this in mind, then, we explore productive ambivalence in everyday data work to understand how uncertainty is leveraged to push activist ideals in a bureaucratic setting. We ask: how might civil servants leverage zones of uncertainty in daily data work to push personal or “radical” agendas using these strategies? How do orientations towards “data” in particular allow them to present a view on new organizational opportunities within “legacy” organizational frameworks? Our ethnographic work begins to provide us with a view of how these concerns figure in the work of the data team.

Civic Data Organizing

The constellation of the aforementioned big data enthusiasms—alongside streams of private sector support, civic-minded investment, and a growing political interest in the administrating potentiality of big data—has engendered formal data teams in urban governance across the globe. On taking office, the mayor of the city of our study sought to lead the way for these global smart cities to certify that the city uses data to solve problems and promote innovation, as stated in the open data mandate. The city thus institutionalized data work in an official mayoral data team (a common configuration for these official data teams), which has been the site of our inquiry.

Despite the data team’s institutionalization, the onus is on the scrappy data team to define what data-driven governance looks like by encouraging personnel in the more than forty departments to switch over from legacy systems and make their work “transparent.” Many share the excitement of potential and possibility through data, but enthusiasm is dampened by tensions surrounding the previously opaque becoming open to scrutiny; once a performance issue is identified, it must be addressed lest it be subject to media attention or citizens’ demands. What “tempered radical” strategies might be enacted by the data team staff members to encourage this leap of faith into data-driven governance? How can they provide public access to the city’s data assets and the more activist ideals of civic participation, justice, and equity?

Small Wins

As Crozier explains, being in a position where one does not have full authority to direct occupational efforts engenders a situation where “each group fights to preserve and enlarge the area upon which it has some discretion” (Crozier 1964, 156). This is certainly true of the tempered radical caught in the crossfire of opposing definitions of data work and civic responsibility. Where the data team makes attempts to define and fortify their open data program throughout the city, other departments are likewise doing the same (i.e. holding onto their old and opaque ways, or even coopting and claiming new forms of data work as their dominion). Nevertheless, the streamlining and opening of city data is precisely the type of organizing work that will allow for other forms of data activism to take place: data resources can be used to advocate for various community needs where, for instance, one is able to see the landscape and distribution of city services. Of course, revealing that one neighborhood is not receiving timely sanitation or security services, for instance, is the sort of visibility that some city departments may be keen to avoid.

Meyerson and Scully (1995) show how the tempered radical strategy of obtaining small wins (instead of, say, a top-down overhaul of civic data work) can be used as trials to “test the boundaries of an organization’s capacity for change.” The city data team members employ such incremental moves to “test the boundaries” of a given department’s ability and willingness to partner in the open data paradigm. A recent instance of this boundary-testing at our field site involved the need to release (via a mandate and pressure from citizen and media data requests), and the desire to automate (so as to avoid manually uploading data every month), the city’s safety data. One data staff member in particular, whom we refer to here as “Dina” (a pseudonym), dedicated herself to procuring this data so as to allow it to, in her words, “speak” about crime patterns across the city with the hope of fostering both discourse and action. Dina met with the data-owners—the city’s police department data team—in person in a series of more casual office visits, coffees, and lunches to explain aims and intentions, and to learn what she could do to facilitate the process. She offered up extra time to jump-start the project with hands-on training, and encouraged regular problem-solving check-ins throughout (a process which later became institutionalized across the city).

The experimental nature of small wins helps to “uncover resources, information, allies, sources of resistance, and additional opportunities for change” (Meyerson and Scully 1995, 595). Rather than insisting the data be fully and immediately released per the top-down mandate, Dina labored continuously to ease concerns, educate, and enhance overall capacities, eradicating at first the infrastructural barriers and later the ideological ones. In the course of these nudging efforts, she also came to learn that prior resistance was enacted for a constellation of less-obvious reasons, namely, legitimate concerns surrounding citizen privacy, technical and personnel bandwidth, and the complicated nature in which data are gathered and stored in the field. This process, then, garnered unlikely allies within the police department itself, data workers who align with her ideals despite the appearance of opposition; these new allies in turn are able to leverage their domain expertise to create space for opening data within broader systems of actual departmental resistance.

Weick suggests that “small wins reduce large problems to a manageable size. Big, unwieldy problems produce anxiety, which limits people’s capacities to think and act creatively” (Meyerson and Scully 1995, 595). Through persistent, smaller projects of familiarization, narrativization, accommodation, and automation, Dina was able to secure the release of the contentious data (contentious as the data points to marginalized neighborhoods with fixed or increasing crime while other neighborhoods are experiencing a decline), paving the way for other highly-desired but potentially problematic datasets to follow suit. This day-to-day organizing work is certainly not part of the city’s open mandate, nor is it the way many other data workers have interpreted what it means to be data-driven; this labor often is invisible and goes unrewarded. However, small wins such as these are a crucial foundation for other efforts of data journalism, data activism, and even internal advocacy, that aims to use the city’s data for political action. For example, owing in particular to the scale and granularity of new forms of data, advocates can discern whether the city is indeed becoming safer for all.

Authentic Action and Language Styles

Dina achieved a cultural shift by “acting in a way that was simply authentic” (Meyerson and Scully 1995, 596). Her earnest interest, in both the release of safety data and in being supportive to those who owned the data, demonstrated an alternative interpretation of the mayor’s open data directive (collective vs. authoritative), and city work writ large which tends to be guarded and competitive. Rolled into the small wins strategy are two other tempered radical approaches: authentic action and language styles. In the conversations regarding safety data, Dina was able to leverage the language of the mandate to open data, and also define the data work as a service and as mutually useful, by couching it in both bureaucratic and progressive frameworks. Her authenticity was perceived and appreciated by others, and even allowed her to forge new partnerships for the open data efforts to come.

However, Dina later stated that she wishes that all of these laborious negotiations (e.g. constant follow-ups, couching transparency in terms of automated workflow efficiency, or framing what the data reveals in a more palatable light) were not necessary; she wishes the data could instead “speak for itself.” This individual considers herself a data activist. She works in the data team less than full time so as to perform other forms of data work in her spare time to achieve activist pursuits. When engaging with the various audiences, she is able to stitch together bureaucratic languages with the vernacular of activists, data scientists, and tech evangelists. She finds that holding these multiple identities and maintaining ambivalence is useful, that “there’s value being both an insider and an outsider.”

Currently, she believes that a lot of the more legible data activism is occurring outside of official offices, but she finds the organizing work done within the data team to be very important for achieving activist ends. When asked what work, if any, is done to ensure external data activists are able to leverage the work done inside city hall, she stated:

“You don’t have to do that much, all you need to do is to put the data out there. Like a lot of people say it [the work that needs to be done to promote social change] is not just open data, like you can’t just do that on its own, but what I’m finding is that you can, as long as you have a data dictionary and that there’s enough data where it’s interesting, people immediately do something with it.”

Multiple Languages and External Affiliations

Meyerson and Scully discuss the importance of external affiliations for tempered radicals (with “people who represent both sides of their identity”), not only to strategize and accumulate useful information, but also for maintaining the ability to remain “fluent in multiple languages.” In another recent instance, the data team’s membership fluency allowed them to take special advantage of an event in the informal civic data-hacking sphere to affect change inside the institution. The advent of new information-processing tools, and broader data literacy in everyday users, has engendered new data cultures such as those located in the volunteer-based civic tech community. Civic data hacking, one such example, “can be framed as a form of data activism and advocacy: requesting, digesting, contributing to, modeling, and contesting data” (Shrock 2016, 4). Many cities utilize such civic tech groups and efforts to enhance the function of their city, or to address social concerns and injustices more directly. In fact, once off the clock, the city employees themselves may also participate in such endeavors to build bridges or exercise their own interests.

One such informal civic data group recently organized around a controversial topic—that of racial discrimination in police procedures—using the “Veil of Darkness” hypothesis. The Veil of Darkness hypothesis “asserts that police are less likely to know the race of a motorist before making a [traffic violation] stop after dark than they are during daylight” (Grogger 2006, 878). Thus, if there is indeed a pattern of racial discrimination, the pattern of traffic stops will differ during the daytime from those conducted at night, where the race of the driver is (supposedly) less readily visible. On the event’s online description, however, the organizer remarked that this more contentious open dataset was recently made inaccessible (password-protected), and those attending the event would now need to collect the data from the organizer who had already downloaded it. This otherwise unexceptional caveat within the event description quickly traveled to city hall.

Employing “multiple languages” (activist language surrounding concerns about racial profiling, together with the language of data science and inclusive of bureaucratic undertones), the team was able “to speak to multiple constituencies” (Meyerson and Scully 1995, 590) about this issue, and leverage the novelty of the situation for their benefit. In the context of this city’s open data efforts, all departments retain control of the datasets they share. That the local civic tech community (and, thus, citizens) noticed and publicized the locking of a particular department’s data became cause for general concern, but there was much uncertainty with regard to what the occasion meant or how it should be dealt with. The data team converted this event into a lever for imploring transparency objectives. They leveraged uncertainty here by highlighting this open data breach to remind stakeholders that these assets (and especially their withdrawal) have the capacity to garner positive or negative citizen and media attention. Thus, the event became an opportunity to apply pressure on appropriate parties to (re)release data, not only in the vein of the open data mandate, but as a means to serve their constituents’ needs and interests. In this sense, the data team was able to use the more familiar language of civil service to encourage the opening of data.

Leveraging external networks like the civic tech community as such exemplifies the affiliations strategy, where those holding dual modes garner the necessary support (emotional, professional, and otherwise) from like-minded peers to buttress against “difficult emotions” and the possibility of burnout (Meyerson and Scully 1995). Members of the data team connect with formal and informal networks that seek to use data for social good to extend their bandwidth, but also to attain support within and outside of the walls of city hall. In a recent interview, Dina reflected on leveraging such a network: civic volunteers (often data scientists or technologists) in the city. She remarked that there appeared to be a boost in interest from citizens (especially those in the civic tech domain), stating that there are: “just a lot of people looking to help the city” via socially-minded data projects. She credited the increased interest to the current political climate; many see this liberal sanctuary city as “a pocket of hope in the nation.”

Local concerns, then, play out against a broader backdrop. The current US presidential administration’s value system is markedly different to predecessors, with differing data policies and demands, notably with regard to immigration and citizenship. The support and enthusiasm shown by citizens and other city data teams helps the team remember why their values are important, providing emotional, technical, and ideological support to buffer against the fatigue incurred from the lack of internal support or recognition, especially at a time of nation-wide consternation.

Tempered Data Organizers

Organizing in the era of big data—and especially big and open data—allows new forms of activism to be layered on top of such efforts. There is a copious amount of work that goes into standardizing and contextualizing data (with meta data, data dictionaries, attributed ownership available for questioning datasets, and so on), as there is with “making it interesting.” Members of the data team believe that this work of making data relevant is their most challenging charge, as it involves procuring vast amounts of interoperable data across city functions and over years before anyone will be able to derive useful meanings from these data resources. In desiring the data to “speak for itself,” the data team is referring to its opposite: the invisible and tedious labor entailed in training, standardization, and meaning-making with data. Tempering data projects with useful interpretations, quality control, and capacity building, allows the data to travel with more ease, making it more accessible and less likely to engender resistance.

Milan and van der Velden discuss how data activism is “polyfunctional” because “it can be read through diverse disciplinary lenses, and can be domesticated to investigate different dynamics and relations, between and within people, information, technology, and the state/industry complex.” Allowing the data to speak for itself does not mean that the data team believes data are wholly objective. Instead, they believe the data are tools to reveal patterns which might help various city workers, civic technologists, non-profits, and data activists speak to matters of concern which might not otherwise be legible.

Although exhausting, organizing from the inside allows for invaluable insight into how and why decisions are made; this has the effect of productively humanizing the opposition, allowing space to introduce activist ideals that may indeed only be impeded by structural ones. By leveraging insider knowledge and strategically pushing zones of uncertainty to engender change, along with being their authentic selves, using variegated language styles, and leveraging external networks, tempered data activists can normalize the exposure and scrutiny involved in releasing massive streams of data to the public, push their own social justice ideals into every day open data work, and allow others outside of centers of power to advocate using these resources.

Conclusion

Open data initiatives in urban governance have been advocated by many in and outside of city government. Values embedded in these programs attract those with more radical ideals about transparency, technology, and civic participation into bureaucracies. However, there are practical realities, and even contradictions, in carrying out such an agenda in the vein of more traditional forms of civic work. We have turned to Crozier’s discussion of the productive uses of uncertainty, and to Meyerson and Scully’s concept of tempered radicalism, to find conceptual resources to account for the practical work of a municipal government data team involved not just in making data work for the city and its citizens, but also charged with figuring out what that might even mean.

Of course, it is easy, particularly for those more actively and visibly radical, to criticize a tempered approach for not going far enough, for not doing all that is possible with data assets, or to accuse those within positions of power of not advocating social change. The authors of the tempered radical concept also take up these concerns, explaining that while judgements of conservativism and hypocrisy abound, the ambivalent position of the tempered radical can play a role in a larger movement outside of themselves and outside of their organizations. They conclude that “the labor of resistance may be divided among those who push for change from the inside, from the outside, and from the margin, each effort being essential to the others and to an overall movement of change” (Meyerson and Scully 1995, 598). Perhaps the world of the tempered data activist in a bureaucratic context can only go so far as that of the data organizer, working tirelessly to systematize institutional data practices to make as much data as accessible as possible, and to normalize ideas of data transparency (whatever that may mean). This role or duty aims to produce a framework and resources for others who own more radical or visibly activist positions, or more fully dedicate their time to activist causes (such as organizations that explicitly work to do so, or data and civic technologists who have more resources at hand in conjunction with less entrenched hierarchies to answer to). Thus, in the broader context of the work that data (and non-data) activists must perform, the work of a tempered radical in this domain, or an activist-as-organizer, is only one, though important, node in the larger data activism assemblage.

The tempered data activist will face additional skepticism—coming from outside the big data movement—as to how well data capture any meaningful truths or experiences of urban citizens, particularly the experiences of those most in need of services and social change. As we noted at the outset, big data itself can only go so far, and it is important to consider what conversations spoken through the language of data might restrict or preclude. In considering tempered radicalism, Meyerson and Scully deliberate on the role of language and its tendency to circumscribe. The authors reveal in one interview that “the power of language was located not in the ability to communicate technically, but rather in its capacity to rule out other forms of talk, thought, and identity.” What possibilities do the current expressions of data assets, technology, and the still unfolding practices of data activism rule out?

Tempered radicals have the advantage of being “outsiders within,” and although this position entails a lot of unaccounted for and uncredited labor, of both a technical and emotional nature, they are also positions of power (fringe identities operating in more traditional power structures are less predictable, and as Crozier shows, there is power in unpredictability). Although she speaks to a more challenging and entrenched position on the margin, bell hooks holds that ambivalent identities offer “the possibility of a radical perspective from which to see and create, to imagine alternative new worlds” (Meyerson and Scully 1995, 589; hooks 1984). The very nature of holding such an identity allows for a vantage point not afforded those well within the margins, though maintaining this ambivalent post certainly has its challenges. What practices—located in everyday negotiations—might foster appreciation and collaboration in and outside of the margin? How can we encourage and better appreciate those embodying these exhausting roles to stick with the discomfort of ambivalence? Given the uncertainty surrounding the possibilities of new data technologies and their application, and their emphasis on vastness and speed, it is crucial to examine the more micro moments of potential as a means to indeed “imagine alternative new worlds,” and to do so incrementally, and collectively.