The final chapter serves to draw the various strands of the book together, surveying what has been discovered, and expanding on the fundamental arguments of the book. It therefore begins with an analysis of Pinterest, which stands as an emblem of all that literacy means in postdigital times, whether that be sophisticated multimodal practices, durational time, or algorithmic logic. Looking back over the screen lives discussed in the book, including those of the crescent voices and of Samuel Sandor, this chapter crystallizes the personal take on screen lives that the book offers, reiterating the need to ‘undo the digital’ and find the human in, on, with, at, and against screens. It also presents some of the problems scholarship must meet, such as digital inequalities, whether that be in terms of time, awareness, or skill with technology. However, despite the considerable negative forces at work in screen lives which the book has taken care to unravel, this concluding chapter advocates ‘taking the higher ground’ and enacting wonder in interactions with screens.

Moving on to AI and algorithms, the penultimate chapter of the book focuses on the importance of vigilance and criticality when engaging with screens. The influence of AI and algorithms on day-to-day interactions, their inherent potential to steal content, and their tendencies to stir up racism and intolerance all mean that it is becoming increasingly vital for researchers, policymakers, and educators to understand these technologies. This chapter argues that being informed and armed with meta-awareness about AI and algorithmic processes is now key to critical digital literacy. In arguing towards this conclusion, it starts by presenting scholarly perspectives and research on AI and literacy, before turning to Ruha Benjamin and Safiya Umoja Noble’s research into racism in AI and algorithms, including Benjamin’s concept of the ‘New Jim Code’. Crescent voices are invoked to contextualize these ideas in real world experiences with algorithmic culture, where encounters with blackboxed practices and struggles to articulate experiences of algorithmic patterns serve to demonstrate further the importance of finding new constructs for critical literacy that encompass algorithmic logic.

Not a day goes by without a new story on the perils of technology. We hear of increasingly clever machines that surpass human capability and comprehension, of tech billionaires imploring each other to stop the ‘out-of-control race’ to produce the most powerful artificial intelligence which poses ‘profound risks to society’, we hear of genetic technologies capable of altering the human genome in ways we cannot predict and a future two-tier humanity consisting of those of us who are genetically enhanced and those who are not. How can we respond to these stories? What should we do politically? By way of exploring these questions (using the UK as the primary example of context), I want to move beyond the usual arguments and legal devices that serve to identify tech developers, and users, as being at fault for individual acts of wrongdoing, recklessness, incompetence or negligence, and ask instead how we might address the broader structural dynamics intertwined with the increasing use of AI and Repro-tech. My argument will be that to take a much sharper structural perspective on these transformative technologies is a vital requirement of contemporary politics.

Humanity’s increasing reliance on AI and robotics is driven by compelling narratives of efficiency in which the human is a poor substitute for the extraordinary computational power of machine learning, the creative competences of generative AI as well as the speed, accuracy and consistency of automation in so many spheres of human activity. Indeed, AI is increasingly becoming the core technological foundation of many contemporary societies. Most thinking on how to manage the downside risks to humanity of this seismic societal shift is set out in a direct fault-based relationship such as the innovative EU AI Act which is by far the most comprehensive political attempt to locate (or deter) those directly responsible for AI-generated harm. I argue that while such approaches are vital for combating injustice exacerbated by AI and robotics, too little thought goes into political approaches to the structural dynamics of AI’s impact on society. By way of example, I examine the UK ‘pro-innovation’ approach to AI governance and explore how it fails to address the structural injustices inherent in increasing AI usage.

The third industrial revolution saw the creation of computers and an increased use of technology in industry and households. We are now in the fourth industrial revolution: cyber, with advances in artificial intelligence, automation and the internet of things. The third and fourth revolutions have had a large impact on health care, shaping how health and social care are planned, managed and delivered, as well as supporting wellness and the promotion of health. This growth has seen the advent of the discipline of health informatics with several sub-specialty areas emerging over the past two decades. Informatics is used across primary care, allied health, community care and dentistry, with technology supporting the primary health care continuum. This chapter explores the development of health informatics as a discipline and how health care innovation, technology, governance and the workforce are supporting digital health transformation.

AI brings risks but also opportunities for consumers. When it comes to consumer law, which traditionally focuses on protecting consumers’ autonomy and self-determination, the increased use of AI also poses major challenges. This chapter discusses both the challenges and opportunities of AI in the consumer context (Section 10.2 and 10.3) and provides a brief overview of some of the relevant consumer protection instruments in the EU legal order (Section 10.4). A case study on dark patterns illustrates the shortcomings of the current consumer protection framework more concretely (Section 10.5).

This chapter discusses how AI technologies permeate the media sector. It sketches opportunities and benefits of the use of AI in media content gathering and production, media content distribution, fact-checking, and content moderation. The chapter then zooms in on ethical and legal risks raised by AI-driven media applications: lack of data availability, poor data quality, and bias in training datasets, lack of transparency, risks for the right to freedom of expression, threats to media freedom and pluralism online, and threats to media independence. Finally, the chapter introduces the relevant elements of the EU legal framework which aim to mitigate these risks, such as the Digital Services Act, the European Media Freedom Act, and the AI Act.

Despite their centrality within discussions on AI governance, fairness, justice, and equality remain elusive and essentially contested concepts: even when some shared understanding concerning their meaning can be found on an abstract level, people may still disagree on their relation and realization. In this chapter, we aim to clear up some uncertainties concerning these notions. Taking one particular interpretation of fairness as our point of departure (fairness as nonarbitrariness), we first investigate the distinction between procedural and substantive conceptions of fairness (Section 4.2). We then discuss the relationship between fairness, justice, and equality (Section 4.3). Starting with an exploration of Rawls’ conception of justice as fairness, we then position distributive approaches toward issues of justice and fairness against socio-relational ones. In a final step, we consider the limitations of techno-solutionism and attempts to formalize fairness by design (Section 4.4). Throughout this chapter, we illustrate how the design and regulation of fair AI systems is not an insular exercise: attention must not only be paid to the procedures by which these systems are governed and the outcomes they produce, but also to the social processes, structures, and relationships that inform, and are co-shaped by, their functioning.

The rules of war, formally known as international humanitarian law, have been developing for centuries, reflecting society’s moral compass, the evolution of its values, and technological progress. While humanitarian law has been successful in prohibiting the use of certain methods and means of warfare, it is nevertheless destined to remain in a constant catch-up cycle with the atrocities of war. Nowadays, the widespread development and adoption of digital technologies in warfare, including AI, are leading to some of the biggest changes in human history. Is international humanitarian law up to the task of addressing the threats those technologies can present in the context of armed conflicts? This chapter provides a basic understanding of the system, principles, and internal logic of this legal domain, which is necessary to evaluate the actual or potential role of AI systems in (non-)international armed conflicts. The chapter aims to contribute to the discussion of the ex-ante regulation of AI systems used for military purposes beyond the scope of lethal autonomous weapons, as well as to recognize the potential that AI carries for improving the applicability of the basic principles of international humanitarian law, if used in an accountable and responsible way.

The main goal of this chapter is to introduce one type of AI used for law enforcement, namely predictive policing, and to discuss the main legal, ethical, and social concerns this raises. In the last two decades, police forces in Europe and in North America have increasingly invested in predictive policing applications. Two types of predictive policing will be discussed: predictive mapping and predictive identification. After discussing these two practices and what is known about their effectiveness, I discuss the legal, ethical, and social issues they raise, covering aspects relating to their efficacy, governance, and organizational use, as well as the impact they have on citizens and society.

Considers the application of machine learning and neural networks to the analysis of big data from bacterial experiments.

Language is the natural currency of most social communication. Until the emergence of more powerful computational methods, it simply was not feasible to measure its use in mainline social psychology. We now know that language can reveal behavioral evidence of mental states and personality traits, as well as clues to the future behavior of individuals and groups. In this chapter, we first review the history of language research in social personality psychology. We then survey the main methods for deriving psychological insights from language (ranging from data-driven to theory-driven, naturalistic to experimental, qualitative to quantitative, holistic to granular, and transparent to opaque) and describe illustrative examples of findings from each approach. Finally, we present our view of the new capabilities, real-world applications, and ethical and psychometric quagmires on the horizon as language research continues to evolve in the future.