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The crisis emerging from the COVID-19 pandemic has elevated the relevance of the welfare state as well as the role of platforms and data infrastructures across key areas of public and social life. Whilst the crisis shed light on the ways in which these might intersect, the turn to data-driven systems in public administration has been a prominent development in several countries for quite some time. In this chapter I focus on the UK as a pertinent example of key trends at the intersection of technological infrastructures and the welfare state. In particular, using developments in UK welfare sectors as a lens, I advance a two-part argument about the ways in which data infrastructures are transforming state-citizen relations through on the one hand advancing an actuarial logic based on personalised risk and the individualisation of social problems (what I refer to as responsibilisation) and, on the other, entrenching a dependency on an economic model that perpetuates the circulation of data accumulation (what I refer to as rentierism). These mechanisms, I argue, fundamentally shift the ‘matrix of social power’ that made the modern welfare state possible and position questions of data infrastructures as a core component of how we need to understand social change.

Depicting the social impact of automated decision systems requires multiple interdisciplinary entry-points. In this paper we focus on the actual data and algorithms that produce specific outputs for the purposes of decision-making. The aim of this report is to outline the range of prominent methods that are used for auditing algorithms in data-driven systems and to also consider some of their limitations.

The Snowden leaks provided unprecedented insights into the workings of state-corporate surveillance programs based on the interception and collection of online activity. They illustrated the extent of “bulk” data collection and the general and widespread monitoring of everyday communication platforms used by ordinary citizens. Yet public response in the United Kingdom and elsewhere has been considerably muted, and there has been little evidence of public outcry, with often conflicting and inconsistent opinions on the subject. Based on research carried out for the project Digital Citizenship and Surveillance Society, this article explores the nuances of public attitudes toward surveillance, including such attitudes among politically active citizens, through focus groups and interviews. We argue that the lack of transparency, knowledge, and control over what happens to personal data online has led to feelings of widespread resignation, not consent, to the status quo that speaks to a condition we identify as “surveillance realism.” We understand this to entail a simultaneous unease among citizens with data collection alongside the active normalization of surveillance that limits the possibilities of enacting modes of citizenship and of imagining alternatives.

In an age of data-driven surveillance, how is civil society impacted? And how have activists sought to resist it?

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.

This project examines the uses of social media for policing domestic extremism and disorder in the UK. The collection and analysis of social media data for the purposes of policing forms part of a broader shift from ‘reactive’ to ‘proactive’ forms of governance in which state bodies engage in big data analysis to predict, preempt and respond in real time to a range of social problems. However, there is a lack of research that accounts for the ways in which different state bodies are making use of big data, and how big data is changing the way states research, prioritize and act in relation to social and political issues. Although big data promises for more efficient, rational and objective decision-making, an important emerging body of work highlight that uses of big data for governance may also contribute to forms of suppression, inequality, and discrimination. What is more, whilst the collection of data may provide opportunities to identify problems and potential ‘threats’, the challenges of oversight, accountability and transparency involved in the collection and use of people’s information have been identified as key concerns. This project engages with these debates by looking specifically at how social media data informs decision-making with regards to the policing of domestic extremism and disorder in the context of the United Kingdom.