Robots have not only become part of our everyday life – they have assumed functions in our criminal justice systems. The following chapters from Sara Beale and Hayley Lawrence, Andrea Roth, Erin Murphy, Emily Silverman, Jörg Arnold, and Sabine Gless focus on evidentiary issues arising from human–robot interaction, while Bart Custers and Lonneke Stevens, as well as David Gray, look at the emerging impact of data protection on criminal justice. This introduction places the chapters in context of the broader issues surrounding the deployment of AI systems in criminal investigations and trials, not all of which are dealt with in the chapters.

In this chapter we summarize the findings of the National Academy of Sciences, Engineering, and Medicine report on proactive policing, focusing on impacts on crime and communities. We begin the paper with a description of how proactive policing was defined in the report, and a summary of the wide scale use of proactive policing approaches in American police agencies. We then turn to summaries of the evidence on crime control and community outcomes. The report concluded that there was sufficient scientific evidence to support the adoption of many proactive policing practices. We argue that successful prevention programs rely on greater focusing of police resources, and expansion of the tools of policing (for crime prevention). The Committee also found that crime prevention outcomes can often be obtained without producing negative community reactions, and that some community-based strategies have begun to show evidence of improving the relations between the police and public. In concluding, we argue that future proactive policing programs should seek to maximize both crime prevention and positive community outcomes and suggest how this can be done given the report’s findings.

Latin America and the Caribbean is the most violent region on earth. However, implementation of evidence-based policing (EBP) lags due to institutional, cultural, political, and skills barriers. This chapter analyzes the context in which EBP is implemented and the challenges encountered and identifies actions to further promote EBP in the region. Challenges to adopt EBP include those present in advanced countries such as the need to institutionalize EBP and to build academic-practitioner partnerships, and also some challenges more prominent in the region including lack of reliable data, instability of political institutions and lack of trust in the police

Law enforcement agencies are increasingly using algorithmic predictive policing systems to forecast criminal activity and allocate police resources. For instance, New York, Chicago, and Los Angeles use predictive policing systems built by private actors, such as PredPol, Palantir and Hunchlab. However, predictive policing is not a panacea to eradicate crime and many concerns raised on the inefficiency, risk of discrimination, as well as lack of transparency. The necessity of protecting fundamental rights has to be reiterated in the algorithmic society. To do it, adapted tools must be deployed to ensure proper enforcement of fundamental rights. Some ethical principles need to be put in place in order to effectively protect fundamental rights and to reinforce them. I argue that while the European constitutional and ethical framework is theoretically sufficient, other tools must be adopted to guarantee the enforcement of Fundamental Rights and Ethical Principles in practice to provide a robust framework, for keeping human rights a central place. Algorithmic Impact Assessment (AIA) constitutes an interesting way to provide a concrete governance of automated decision-making.

This is the introductory chapter to the edited collection on 'Data-Driven Personalisation in Markets, Politics and Law' (Cambridge University Press, 2021) that explores the emergent pervasive phenomenon of algorithmic prediction of human preferences, responses and likely behaviours in numerous social domains – ranging from personalised advertising and political microtargeting to precision medicine, personalised pricing and predictive policing and sentencing. This chapter reflects on such human-focused use of predictive technology, first, by situating it within a general framework of profiling and defends data-driven individual and group profiling against some critiques of stereotyping, on the basis that our cognition of the external environment is necessarily reliant on relevant abstractions or non-universal generalisations. The second set of reflections centres around the philosophical tradition of empiricism as a basis of knowledge or truth production, and uses this tradition to critique data-driven profiling and personalisation practices in its numerous manifestations.

This chapter addresses autonomy’s role in democratic governance. Political authority may be justifiable or not. Whether it is justified and how it can come to be justified is a question of political legitimacy, which is in turn a function of autonomy. We begin, in section 8.1, by describing two uses of technology: crime predicting technology used to drive policing practices and social media technology used to influence elections (including by Cambridge Analytica and by the Internet Research Agency). In section 8.2 we consider several views of legitimacy and argue for a hybrid version of normative legitimacy based on one recently offered by Fabienne Peter. In section 8.3 we explain that the connection between political legitimacy and autonomy is that legitimacy is grounded in legitimating processes, which are in turn based on autonomy. Algorithmic systems—among them PredPol and the Cambridge Analytica-Facebook-Internet Research Agency amalgam—can hinder that legitimation process and conflict with democratic legitimacy, as we argue in section 8.4. We conclude by returning to several cases that serve as through-lines to the book: Loomis, Wagner, and Houston Schools.

What is critical thinking? To paraphrase the Enlightenment philosopher Immanuel Kant, it is the emergence from one’s self-imposed nonage. Nonage is the inability to use one’s mind without another’s guidance. This inability is self-imposed if its cause lies not in the limits of one’s mind but in the lack of courage to use it independently, without others’ guidance. Yet, in the age of powerful algorithms that play better chess and Go than humans, recommend the music and books we like, predict criminal behavior, and even find us the ideal romantic partner, why would we still need to think critically? Would it not be more economical to cease wasting time on thinking and reflecting, and just click and like? I argue that we need more, not less, critical thinking in the digital age. I discuss several tools for critical thinking, including asking the right questions and detecting misleading statistics, and illustrate these by online dating sites, HIV tests, cancer diagnosis, big data predictive analytics, the Social Credit System, and more. Advances in technology require risk-literate people who can control digital media rather be controlled by it.

Automated profiling, predictive analytics, and data mining represent the extension of nineteenth-century statistical models through the lens of computation. Algorithmic pattern matching also has origins in supermarket management, as well as the discipline of operations research that developed through the Vietnam War. When applied to law enforcement, policing, and criminal justice, this has led to a plethora of systems used by governments, typically developed by private companies, used for intelligence, predictive policing, and criminal justice risk assessments. This chapter argues that the establishment and proliferation of these tools has cemented the primacy of statistical knowledge systems as the primary systems of evaluation by which individuals are interpreted and known by states.