This chapter introduces abstract dynamic programs and shows how this framework can be used to prove the core optimality results from the book.

Disinformation and the spread of false information online have become a defining feature of social media use. While this content can spread in many ways, recently there has been an increased focus on one aspect in particular: social media algorithms. These content recommender systems provide users with content deemed ‘relevant’ to them but can be manipulated to spread false and harmful content. This chapter explores three core components of algorithmic disinformation online: amplification, reception and correction. These elements contain both unique and overlapping issues and in examining them individually, we can gain a better understanding of how disinformation spreads and the potential interventions required to mitigate its effects. Given the real-world harms that disinformation can cause, it is equally important to ground our understanding in real-world discussions of the topic. In an analysis of Twitter discussions of the term ‘disinformation’ and associated concepts, results show that while disinformation is treated as a serious issue that needs to be stopped, discussions of algorithms are underrepresented. These findings have implications for how we respond to security threats such as a disinformation and highlight the importance of aligning policy and interventions with the public’s understanding of disinformation.

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.

Opening with an analysis of Instagram, Chapter 2 is concerned with how to think about postdigitality. Touching on multimodality, time-space-place, and responsive loops, this chapter highlights the contrast between digital life and postdigital life, unravelling the many dimensions of postdigitality. It concludes that postdigitality represents a world of symbiosis, whether that be of body and mind, physical life and screen life, representation and non-representation, immersion and connectivity, or interaction and convergence. These combinations are what lends digital media its unique power to move across time, space, and place. To explore these ideas, Chapter 2 analyses data which has been processed through ATLAS.ti to produce a list of postdigital keywords used by crescent voices.

Chapter 4 delves deeper into screen life, adopting an even more human-centred focus, in order to uncover the affective aspect of screen lives. Maintaining an embodied approach, this chapter explores how affective experiences with screens are intentionally elicited through how media is designed, how affect on screens might differ from affect outside screens, and how digital affect can inform practices, and practices induce affect. The chapter begins by defining affect, then digital affect more specifically, before turning to interviewees for their perspectives on how they feel and sense on screens, touching on topics such as micro digital affect, algorithms, and the pandemic. Crescent voices in this chapter help illustrate how digital affect is vital to understanding digital literacy practices and screen lives, especially the double-edged aspects of our affective relationships to 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.

This chapter examines ways in which longstanding features of the legal system serve to counteract the forces outlined in Chapters 2 and 3 and thereby minimize the influence of improper factors on judicial behavior. It considers the adversarial process, the doctrine of precedent (or stare decisis), and the practice of justifying decisions via written opinions, and examines the ways in which the nature of each – and thus its effectiveness in channeling judges – has decreased. It further explores changes in the practice of law and in the culture more generally, including automation, that have altered the manner and depth in which lawyers and judges engage with the law.

As its name indicates, algorithmic regulation relies on the automation of regulatory processes through algorithms. Examining the impact of algorithmic regulation on the rule of law hence first requires an understanding of how algorithms work. In this chapter, I therefore start by focusing on the technical aspects of algorithmic systems (Section 2.1), and complement this discussion with an overview of their societal impact, emphasising their societal embeddedness and the consequences thereof (Section 2.2). Next, I examine how and why public authorities rely on algorithmic systems to inform and take administrative acts, with special attention to the historical adoption of such systems, and their impact on the role of discretion (Section 2.3). Finally, I draw some conclusions for subsequent chapters (Section 2.4).

This last chapter summarizes most of the material in this book in a range of concluding statements. It provides a summary of the lessons learned. These lessons can be viewed as guidelines for research practice.

Network science is a broadly interdisciplinary field, pulling from computer science, mathematics, statistics, and more. The data scientist working with networks thus needs a broad base of knowledge, as network data calls for—and is analyzed with—many computational and mathematical tools. One needs good working knowledge in programming, including data structures and algorithms to effectively analyze networks. In addition to graph theory, probability theory is the foundation for any statistical modeling and data analysis. Linear algebra provides another foundation for network analysis and modeling because matrices are often the most natural way to represent graphs. Although this book assumes that readers are familiar with the basics of these topics, here we review the computational and mathematical concepts and notation that will be used throughout the book. You can use this chapter as a starting point for catching up on the basics, or as reference while delving into the book.