Chapter 9 draws on the evidence outlined earlier in the book to evaluate a range of possible legal interventions. Structured according to the five potential equality objectives outlined earlier, the measures include steps to increase the visibility of people with disfigurements in daily life, methods of motivating employers to become appearance-inclusive and changes to influential institutions outside the employment context. They also include a range of legislative reforms to replace the severe disfigurement provision with a better remedial mechanism, such as the creation of a new protected characteristic of disfigurement or the reformulation of the definition of disability.

From an “infrastructural gaze,” this chapter examines the penetration of artificial intelligence (AI) in capital markets as a blend of continuity and change in finance. The growing infrastructural dimension of AI originates first from the evolution of algorithmic trading and governance, and second, from its ascent as a “general-purpose technology” within the financial domain. The text discusses the consequences of this “infrastructuralization” of financial AI, considering the micro–macro tension typical of capital accumulation and crisis dynamics. Challenging the commonly espoused notion of AI as a stabilizing force, the analysis underscores its connections with volatile, crisis-prone financialized dynamics. It concludes by outlining potential consequences (unpredictability, operational inefficiency, complexity, further concentration) and (systemic) risks arising from the emergence of AI as a “new” financial infrastructure, particularly those related to biases in data and data commodification, lack of explanation of underlying models, algorithmic collusion, and network effects. The text asserts that a thorough understanding of these hazards can be attained by adopting a perspective that considers the macro/meso/micro connections inherent in infrastructures.

The complexity involved in developing and deploying artificial intelligence (AI) systems in high-stakes scenarios may result in a “liability gap,” under which it becomes unclear who is responsible when things go awry. Scholarly and policy debates about the gap and its potential solutions have largely been theoretical, with little effort put into understanding the general public’s views on the subject. In this chapter, we present two empirical studies exploring laypeople’s perceptions of responsibility for AI-caused harm. First, we study the proposal to grant legal personhood to AI systems and show that it may conflict with laypeople’s policy preferences. Second, we investigate how people divide legal responsibility between users and developers of machines in a variety of situations and find that, while both are expected to pay legal damages, laypeople anticipate developers to bear the largest share of the liability in most cases. Our examples demonstrate how empirical research can help inform future AI regulation and provide novel lines of research to ensure that this transformative technology is regulated and deployed in a more democratic manner.

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.