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What can urban big data research tell us about cities? While studying cities as complex systems offers a new perspective on urban dynamics, we should dig deeper into the epistemological claims made by these studies and ask what it means to distance the urban researcher from the city. Big data research has the tendency to flatten our perspective: it shows us technology users and their interactions with digital systems but does so often at the expense of the informal and irregular aspects of city life. It also presents us with the city as optimisable system, offering up the chance to engineer it for particular forms of efficiency or productivity. Both optimisation itself, and the process of ordering of the city for optimisation, confer political and economic power and produce a hierarchy of interests. This commentary advocates that researchers connect systems research to questions of structure and power. To do this requires a critical approach to what is missing, what is implied by the choices about which data to collect and how to make them available, and an understanding of the ontologies that shape both the data sets and the urban spaces they describe.

Digital vaccination certification involves making many promises, few of which can realistically be kept. In this paper, we demonstrate how this phenomenon constitutes various forms of theatre – immunity theatre, border theatre, behavioural theatre and equality theatre – doing so by drawing on perspectives from technology regulation, migration studies and critical geopolitics. Technological theatre and political theatre often serve valid functions, but these forms are problematic for several reasons. First, they involve real-world infrastructures that, while unlikely to accomplish the task at hand, will nevertheless last a long time and be repurposed. They therefore constitute governance by data infrastructure, diverting action and control away from elected legislators to for-profit contractors. Second, vaccine certification effectively legitimises inequalities between countries and people by formalising ways to distinguish between the vaccinated and non-vaccinated and to exclude the latter, thus reinforcing both mobility and connectivity divides. It serves as a way to (further) close borders and to regulate, through code and infrastructure, access to public goods such as employment and public space. Finally, the project of certification displaces a more important action, namely addressing the radical inequality in countries’ ability to combat the pandemic.

Crisis narratives surrounding Europe's 2015 migration influx fuelled demands for new ways of tracking, mapping and predicting human mobility. We explore how market opportunities for technology firms and data analytics start-ups created by the EU's Global Approach to Migration led to solutionistic approaches to compiling and analysing migration statistics. We show that initiatives such as the rebranding of existing platforms and services as migration prediction systems are consolidating policy conceptualisations of migration as risk. Despite the promise of greater granularity, this "big data approach" cannot offer greater certainty about who is on the move and why. Instead such approaches are ill-suited to understanding the complex dynamics of migration and to offering protection to vulnerable people. The marketisation of migration statistics through big data offers a key case for advancing progressive approaches to both migration statistics and global data justice.Los relatos de crisis en torno a las corrientes migratorias hacia Europa del año 2015 han impulsado la demanda de nuevas formas de seguimiento, mapeo y predicción de la movilidad de personas. Exploramos cómo las oportunidades de mercado para las empresas de tecnología y las nuevas empresas de análisis de datos creadas por el Enfoque Global de la Migración de la UE han causado enfoques simplificadores al compilar y analizar las estadísticas migratorias. Mostramos que iniciativas tales como el cambio de nombre de ciertas plataformas y servicios existentes en los sistemas de predicción migratoria están transformando la conceptualización de las política migratoria como un riesgo. A pesar de la promesa de una mayor granularidad, este “enfoque big data” no logra ofrecer una mayor certeza sobre quién está en movimiento y por qué. Al contrario, estos enfoques no son adecuados para comprender las complejas dinámicas migratorias ni para ofrecer protección a las personas vulnerables. La comercialización de las estadísticas migratorias a través de Big Data ofrece un caso clave para avanzar sobre enfoques más progresivos tanto para las estadísticas migratorias y como para la justicia global de datos.

As data technologies become the medium of experiment for living labs, they become less a collaboration between citizen and researcher and more a test of how commercial actors can influence the public. Two new practices suggest we shouldapply research ethics rules: first, that the experimentation taking place does not aim to test technology using people, but to test people using technology; and second, that such experimentation is explicitly designed to understand how thepopulation outside the lab can be influenced and manipulated, and therefore has a political character that research ethics can give us some leverage over.

Biometrics, the technology for measuring, analysing and processing a person’s physiological characteristics, such as their fingerprints, iris or facial patterns, is increasingly used in the management of migrant and refugee flows. This panel interrogates the uses of biometric technologies and the consequences for the lives of migrants and refugees. It asks how biometric data are constituted, what their limitations and biases are, how biometric technologies challenge traditional notions of the physical border, in whose interest and with what implications for migrants and refugees. In particular, in bringing together a multidisciplinary group of international experts to develop a critical, comparative and empirically grounded dialogue, the panel explores the consequences of this ‘machinic life’ for the lives of actual people, migrants and refugees who navigate actual and digital borders in the quest of a better life. As such, the panel engages with crucial themes of processes of bordering, extractive logics and commercial dimensions of biometric flows and algorithmic sorting, discrimination and exclusion, and human agency and autonomy. Ultimately, all papers are concerned with the broader intersection of data, computation and justice.

The scale and asymmetry of commercial technology firms’ power over people through data, combined with the increasing involvement of the private sector in public governance, means that increasingly, people do not have the ability to opt out of engaging with technology firms. At the same time, those firms are increasingly intervening on the population level in ways that have implications for social and political life. This creates the potential for power relations of domination, and demands that we decide what constitutes the legitimacy to act on the public. Business ethics and private law are not designed to answer these questions, which are primarily political. If people have lost the right to disengage with commercial technologies, we may need to hold the companies that offer them to the same standards to which we hold the public sector. This paper first defines the problem and demonstrates that it is significant and widespread, and then argues for the development of an overarching normative framework for what constitutes non-domination with regard to digital technologies. Such a framework must involve a nuanced idea of political power and accountability that can respond not only to the legality of corporate behaviour, but to its legitimacy.

The increasing availability of digital data reflecting economic and human development, and in particular the availability of data emitted as a by-product of people’s use of technological devices and services, has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been explicitlyconnected to a social justice agenda by the agencies and authorities involved. Meanwhile, although data-driven discrimination is advancing at a similar pace to data processing technologies, awareness and mechanisms for combating it are not. This paper posits that just as an idea of justice is needed in order to establish the rule of law, an idea of data justice – fairness in the way people are made visible, represented and treated as a result of their production of digital data – is necessary to determine ethical paths through a datafying world. Bringing together the emerging scholarly perspectives on this topic, I propose three pillars as the basis of a notion of international data justice: (in)visibility, (dis)engagement with technology and antidiscrimination. These pillars integrate positive with negative rights and freedoms, and by doing so challenge both the basis of current data protection regulations and the growing assumption that being visible through the data we emit is part of the contemporary social contract.

In this paper, we investigate people’s perception of datafication andsurveillance in Amsterdam Smart City. Based on a series of focusgroups, we show how people understand new forms of hypervisbi-lity, what strategies they use to navigate these experiences, and whatthe limitations of these strategies are. We show how people tried todiscern between public and private sector actors, to differentiate whothey trusted by building on the existing social contract. People alsotrusted the objectivity of data in relation to prior experiences of socialcontexts and discrimination. Lastly, we show how the experiences ofsome of the inhabitants in our study who were most vulnerable tohypervisibility highlight the limits to strategies based on the neutral-ity of data. By asking about perceived surveillance rather thanemphasising actual practices of surveilling, we show differentiatedcontexts and strategies, providing empirical grounds to question thedominant technical framing of smart cities.

Today, withdrawing from digital space would mean giving up on countless pleasures and conveniences afforded by communication and navigation devices. Even if one wanted to withdraw, living offline has become rather difficult. Around the world urban life has become digitized and datafied to a degree that any attempt at living even for a few days without engagement with digital space would likely require a withdrawal from urban life altogether: from travel, work, and personal relations as we nowadays experience and live them. Many urbanites produce digital data through almost everything they do. We get up in the morning and use a mobile phone that is constantly emitting information to check our email, the news, and social media. We travel to work using an electronic travel card or in a car with various GPS and digital systems. We walk down streets where signals from our phones and other devices are captured and read by wifi beacons and MAC address sensors, and our images by CCTV. We use apps that emit details of our location, we tweet, we tag, we check in. We make phone calls through particular antennas set up by our mobile phone providers. We interact with the city digitally by paying our taxes, living in our houses, using city services, and offering feedback to the authorities. All day, digital signatures are embedded in the technologies we use, emitted as we communicate and move around, and signaled by most of our activities. The picture that builds up about us in the course of every day is both behavioral and spatial in ways that are often opaque to us. Actual and possible effects of digitalization and datafication of urban life are critically debated in scholarly and policy circles; and Liesbet Van Zoonen (2015) has observed that city governments today are faced with a super-wicked problem of data governance (Levin et al. 2012). In this context citizens are both contributors to the digitalization and datafication, as well as being affected by these processes.

We examine the current ‘datafication’ process underway in low- and middle-income countries (LMICs), and the power shifts it is creating in the field of international development. The use of new communications and database technologies in LMICs is generating ‘big data’ (for example from the use of mobile phones, mobile-based financial services and the internet) which is collected and processed by corporations. When shared, these data are also becoming a potentially valuable resource for development research and policy. With these new sources of data, new power structures are emerging within the field of development. We identify two trends in particular, illustrating them with examples: first, the empowerment of public–private partnerships around datafication in LMICs and the consequently growing agency of corporations as development actors. Second, the way commercially generated big data is becoming the foundation for country-level ‘data doubles’, i.e. digital representations of social phenomena and/or territories that are created in parallel with, and sometimes in lieu of, national data and statistics. We explore the resulting shift from legibility ( Scott, 1998 ) to visibility, and the implications of seeing development interventions as a byproduct of larger-scale processes of informational capitalism.

This paper examines research about Wikipedia that has been undertaken using big data approaches. The aim is to gauge the coherence as against the disparateness of studies from different disciplines, how these studies relate to each other, and to research about Wikipedia and new social media in general. The paper is partly based on interviews with big data researchers, and discusses a number of themes and implications of Wikipedia research, including about the workings of online collaboration, the way that contributions mirror (or not) aspects of real-world geographies, and how contributions can be used to predict offline social and economic trends. Among the findings is that in some areas of research, studies build on and extend each other's results. However, most of the studies stay within disciplinary silos and could be better integrated with other research on Wikipedia and with research about new media. Wikipedia is among few sources in big data research where the data are openly available, unlike many studies where data are proprietary. Thus, it has lent itself to a burgeoning and promising body of research. The paper concludes that in order to fulfil this promise, this research must pay more attention to theories and research from other disciplines, and also go beyond questions based narrowly on the availability of data and towards a more powerful analytical grasp of the phenomenon being investigated.

This paper is the product of a workshop that brought together practitioners, researchers, and data experts to discuss how big data is becoming a resource for positive social change in low- and middle-income countries (LMICs). We include in our definition of big data sources such as social media data, mobile phone use records, digitally mediated transactions, online news media sources, and administrative records. We argue that there are four main areas where big data has potential for promoting positive social change: advocacy; analysis and prediction; facilitating information exchange; and promoting accountability and transparency. These areas all have particular challenges and possibilities, but there are also issues shared across them, such as open data and privacy concerns. Big data is shaping up to be one of the key battlefields of our time, and the paper argues that this is therefore an opportune moment for civil society groups in particular to become a larger part of the conversation about the use of big data, since questions about the asymmetries of power involved are especially urgent in these uses in LMICs. Civil society groups are also currently underrepresented in debates about privacy and the rights of technology users, which are dominated by corporations, governments and nongovernmental organizations in the Global North. We conclude by offering some lessons drawn from a number of case studies that represent the current state-of-the-art.

While plans to develop “smart cities” are gathering pace across the world, we know little about the ways in which the discourses of datafication, smartness, and big data play out in material contexts of urban development, including utility and resource management. In this paper, we explore this intersection in the case of Bangalore’s water supply, where IBM in alliance with the Bangalore Water Supply and Sewerage Board (BWSSB) is implementing a water-flow sensor network and geographic database system under the label of “big data for water supply.” We illustrate how the BWSSB-IBM approach narrows down the complex field of water provision to a question of water in- and out-flow measurements and the monitoring of BWSSB ground personnel. In theoretical terms, we discuss the ways in which these processes constitute both particular claims to knowledge, and the redefinition of citizenship as consumption.

The increasing availability of ‘data fumes’ (Thatcher, 2014) – data produced as a byproduct of people’s use of technological devices and services – has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been explicitly connected to a social justice agenda. In fact, while data-driven discrimination is advancing at exactly the same pace as data processing technologies, awareness and mechanisms for combating it are not. This paper posits that just as an idea of justice is needed in order to establish the rule of law, an idea of data justice is necessary to determine ethical paths through a datafying world. The paper will analyse the existing work on data justice and argue for a framework in which it can be brought together into a single framing for further research and debate.

The use of digital communication technologies, and of mobile phones in particular, has seen an exponential rise in low- and middle-income countries over the last decade. These data, emitted as a byproduct of technologies such as mobile phone location information and calling metadata, have the potential to fill some of the problematic gaps in data resources available to country policymakers and international development organisations. Using three examples of current big data initiatives in the international development field, we examine the implications of these new types of data for development policy and planning: their advantages and drawbacks, emerging practices relating to their use, and how they potentially influence ideas and policies of development. We also assess the politics of these new types of digital data, which are often collected and processed by corporations or by researchers in industrialised countries. Our analysis indicates that these new data sources already represent an important complement to country-level statistics, but that there are currently important challenges which will need to addressed if the promises of big data in development are to be fulfilled.

This chapter draws together the conclusions of the book as a whole, namely that the available typologies of group privacy, such as collective action lawsuits or the articulation of the rights of political or activist groups, are intuitively insufficient to address the landscape emerging from the new data analytic technologies. The book has demonstrated that there are multiple and often divergent perspectives on what a group is and how it should be addressed with regard to privacy, but this divergence is also an important tool for understanding which elements of the problem can be addressed using current legal and conceptual tools and which will require new approaches. The chapter outlines the authors’ contributions to the typology of group privacy concerns, then identifies the gaps and limitations that arise from a group perspective on privacy, and the conceptual and practical implications of taking the group level into account. Finally, it suggests ways forward for future research, discussion and action.

The introduction outlines the origin of the book in the concerns arising from new data analytical technologies with regard to collectives. Although their effects have so far been addressed only on the individual level, in fact profiling and machine learning technologies are directed at the group level and are used to formulate types, not tokens – they work to scale, and enable their users to target the collective as much as the individual. This means that our legal, philosophical and analytic attention to the individual may need to be adjusted, and possibly extended, in order to pay attention to the actual technological landscape unfolding before us. This book represents such an adjustment: it may be seen as an exploration of the territory that lies between ‘their privacy’ and ‘its privacy’, with regard to a given group. In this book we push the boundary towards ‘its’, and begin to think about the implications of that shift, first by identifying who must be involved in the discussion. This chapter outlines the philosophical and legal concerns that arise from considering group privacy, and sets out the challenge that this volume aims to answer.

Zero-rating has become a perfect storm incorporating politics, profit, and technical complexity. The claim that everyone has a right to Internet connectivity is coming into conflict with the argument for net neutrality: will the provision of free data services via mobile phones worldwide privilege a small minority of content providers such as Facebook and Google, or will it provide valuable access to information and services for low-income people worldwide? This dichotomy, however, is based on a reductivist perspective that doesn't take into account the possible disadvantages of a reliance on mobile connectivity for a majority of the world's citizens. If we add geographical and economic context into the debate, it becomes apparent that zero-rating could benefit some, but could also impoverish and further disadvantage many. Who are the winners and losers from a global perspective, and what are their chances against the prevailing wisdom of Silicon Valley?

Questions of data and algorithmic analysis saturated the conference at which Dalton and Thatcher first called for a Critical Data Studies (Dalton and Thatcher, 2014a). Soon thereafter, Taylor published a response to their original call (Dalton and Thatcher, 2014b) that focused on the spatial nature of data and the need to critically contextualize it (Taylor, 2015a). This was one of multiple beginnings of Critical Data Studies, as it will always be three words cobbled together imperfectly signifying diverse sets of work around data’s recursive relationship to society (c.f. Graham 2014; Kitchin 2014, and elsewhere). We highlight that moment not to claim the concept, but to contextualize the focus of this co-interview. When Taylor published their response to Dalton and Thatcher, she focused on the specifically spatial nature of much data, not only in the content of said data, but also in the need to contextualize from where it originates and is put to use. Data varies across space in a variety of ways: from those included or excluded to those who access said data for analysis, manipulation and (re)presentation.Critical Data Studies calls attention to subject formation within these data regimes, for a critical examination of where the interpellation of the individual emerges in algorithmic culture (Striphas, 2015) and, through that, where the cracks and seams, the spaces for resistance and alternatives, might be found. When you append “critical” to a field of study, you run the risk of both offending other researchers, who rightly point out that all research is broadly critical and of bifurcating those who use critical theory from those who engage in rigorous empirical research. Kate Crawford recently observed that the meaning of Critical Data Studies is as political as the data it engages (Crawford, 2015). To extend that, it must remain contestable in order to contest the creation, commodification, analysis, and application of data. Ultimately, Critical Data Studies must make space for the recursive dialogue between the deeply theoretical and the robustly empiric and, in so doing, avoid the hubris of pseudopositivism and technological determinism, in favor of the nuanced and contingent.This is a dialogue between Dalton, Thatcher, and Taylor on the spatial nature of data with respect to Critical Data Studies. It highlights the historical variability of the processes of data production and accumulation and how this, in turn, has resulted in the uneven development of data. We ask both what is missing and what must be brought to the analysis of data in order to respond to the existence of particular dataspheres governed by the kinds of technology available in different locations. In form, Dalton and Thatcher ask the first question and Taylor responds before asking the second, alternating from there. As such, this dialog has many entry and exit points and is not meant as a definitive statement of what Critical Data Studies is or who can speak for it. Rather, we choose to live in the unresolved tensions between researcher and subject, technology and society, space and time (Haraway, 1991), and we encourage our readers to do the same.

A report on the use and governance of digital data by the city of Amsterdam, with policy recommendations towards inclusive data governance that is fair to minorities and less privileged groups.

Although the terminology of Big Data has so far gained little traction in economics, the availability of unprecedentedly rich datasets and the need for new approaches – both epistemological and computational – to deal with them is an emerging issue for the discipline. Using interviews conducted with a cross-section of economists, this paper examines perspectives on Big Data across the discipline, the new types of data being used by researchers on economic issues, and the range of responses to this opportunity amongst economists. First, we outline the areas in which it is being used, including the prediction and ‘nowcasting’ of economic trends; mapping and predicting influence in the context of marketing; and acting as a cheaper or more accurate substitute for existing types of data such as censuses or labour market data. We then analyse the broader current and potential contributions of Big Data to economics, such as the ways in which econometric methodology is being used to shed light on questions beyond economics, how Big Data is improving or changing economic models, and the kinds of collaborations arising around Big Data between economists and other disciplines.

In the expansion of health ecosystems, issues of responsibility and sustainability of the data science involved are central. The idea that these values should be central to the practice of data science is increasingly gaining traction, yet there is no agreement on what exactly makes data science responsible or sustainable because these concepts prove slippery when applied to a global field involving commercial, academic and governmental actors. This lack of clarity is causing problems in setting goals and boundaries for data scientific practice, and risks fundamental disagreement on governance principles for this emerging field. We will argue in this commentary for a commons analytical framework as one approach to this problem, since it offers useful signposts for how to establish governance principles for shared resources.