The Snowden leaks, first published in June 2013, provided unprecedented insights into the operations of state-corporate surveillance, highlighting the extent to which everyday communication is integrated into an extensive regime of control that relies on the 'datafication' of social life. Whilst such data-driven forms of governance have significant implications for citizenship and society, resistance to surveillance in the wake of the Snowden leaks has predominantly centred on techno-legal responses relating to the development and use of encryption and policy advocacy around privacy and data protection. Based on in-depth interviews with a range of social justice activists, we argue that there is a significant level of ambiguity around this kind of anti-surveillance resistance in relation to broader activist practices, and critical responses to the Snowden leaks have been confined within particular expert communities. Introducing the notion of 'data justice', we therefore go on to make the case that resistance to surveillance needs to be (re)conceptualized on terms that can address the implications of this data-driven form of governance in relation to broader social justice agendas. Such an approach is needed, we suggest, in light of a shift to surveillance capitalism in which the collection, use and analysis of our data increasingly comes to shape the opportunities and possibilities available to us and the kind of society we live in.

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Drawing on the first comprehensive investigation into the uses of data analytics in UK public services, this article outlines developments and practices surrounding the upsurge in data-driven forms of what we term 'citizen scoring'. This refers to the use of data analytics in government for the purposes of categorisation, assessment and prediction at both individual and population level. Combining Freedom of Information requests and semi-structured interviews with public sector workers and civil society organisations, we detail the practices surrounding these developments and the nature of concerns expressed by different stakeholder groups as a way to elicit the heterogeneity, tensions and negotiations that shape the contemporary landscape of data-driven governance. Described by practitioners as a way to achieve a 'golden view' of populations, we argue that data systems need to be situated in this context in order to understand the wider politics of such a 'view' and the implications this has for state-citizen relations in the scoring society.

This article analyses three distinct child welfare data systems in England. We focus on child welfare as a contested area in public services where data systems are being used to inform decisionmaking and transforming governance. We advance the use of "data assemblage" as an analytical framework to detail how key political and economic factors influence the development of these data systems. We provide an empirically grounded demonstration of why child welfare data systems must not be considered neutral decision aid tools. We identify how systems of thought, ownership structures, policy agendas, organizational practices, and legal frameworks influence these data systems. We find similarities in the move toward greater sharing of sensitive data, but differences in attitudes toward public-private partnerships, rights and uses of prediction. There is a worrying lack of information available about the impacts of these systems on those who are subject to themparticularly in relation to predictive data systems. We argue for policy debates to go beyond technical fixes and privacy concerns to engage with fundamental questions about the power dynamics and rights issues linked to the expansion of data sharing in this sector as well as whether predictive data systems should be used at all.

Social media and big data uses form part of a broader shift from ‘reactive’ to ‘proactive’ forms of governance in which state bodies engage in analysis to predict, pre-empt and respond in real time to a range of social problems. Drawing on research with British police, we contextualize these algorithmic processes within actual police practices, focusing on protest policing. Although aspects of algorithmic decision-making have become prominent in police practice, our research shows that they are embedded within a continuous human–computer negotiation that incorporates a rooted claim to ‘professional judgement’, an integrated intelligence context and a significant level of discretion. This context, we argue, transforms conceptions of threats. We focus particularly on three challenges: the inclusion of pre-existing biases and agendas, the prominence of marketing-driven software, and the interpretation of unpredictability. Such a contextualized analysis of data uses provides important insights for the shifting terrain of possibilities for dissent.