Machine Learning for Criminology and Crime Research: At the Crossroads reviews the roots of the intersection between machine learning, artificial intelligence (AI), and research on crime; examines the current state of the art in this area of scholarly inquiry; and discusses future perspectives that may emerge from this relationship.
As machine learning and AI approaches become increasingly pervasive, it is critical for criminology and crime research to reflect on the ways in which these paradigms could reshape the study of crime. In response, this book seeks to stimulate this discussion. The opening part is framed through a historical lens, with the first chapter dedicated to the origins of the relationship between AI and research on crime, refuting the "novelty narrative" that often surrounds this debate. The second presents a compact overview of the history of AI, further providing a nontechnical primer on machine learning. The following chapter reviews some of the most important trends in computational criminology and quantitatively characterizing publication patterns at the intersection of AI and criminology, through a network science approach. This book also looks to the future, proposing two goals and four pathways to increase the positive societal impact of algorithmic systems in research on crime. The sixth chapter provides a survey of the methods emerging from the integration of machine learning and causal inference, showcasing their promise for answering a range of critical questions.
With its transdisciplinary approach, Machine Learning for Criminology and Crime Research is important reading for scholars and students in criminology, criminal justice, sociology, and economics, as well as AI, data sciences and statistics, and computer science.
Gian Maria Campedelli is a research fellowship holder at the Department of Sociology and Social research of the University of Trento
From Introduction (pagg. 1-3)
The Novelty Narrative - An Unorthodox Introduction
One of the most common ways to advertise a scientific product, be it an idea, an article, or a book, is to describe it as “novel.” Novel theories might encounter resistance in a certain academic field, yet they attract curiosity, and curiosity is enough to make them spread, intellectually and commercially, provided of course that such theories are sufficiently engaging and plausible. The same goes for applications or use cases, granted that they are not only “novel” but also interesting to a considerable portion of the audience they are directed to. Addressing “novel” research problems through a given methodology fosters the interest of peers in our research community, leading to higher dissemination, debate, and discussion. The same applies to “novel” methods. If they are, to some extent demonstrated to be useful and provide advantages over alternative methodological approaches, branding the empirical approaches we develop in our scholarship as “novel” can help us captivate the attention of our colleagues. In contemporary science, “novelty” is many times used as a marketing strategy to increase the attractiveness of a scientific product, and it is often one of those features upon which one work is judged. On the low-end of a seniority spectrum, graduate students’ dissertations are also judged in terms of novelty. On the opposite end, even Nobel prizes are awarded taking into consideration the novelty of their contributions to certain problems.
It is therefore undoubtedly tempting to construct a “novelty”-based narrative around the contents of this book. Most readers or commentators would not argue otherwise. My strategy would not be seen as a marketing one alone, but rather as a motivated, perfectly reasonable decision. In fact, criminologists and crime researchers may have already heard or read several times that “machine learning” represents a novel methodological toolbox to address crime-related problems. This is the dominant narrative depicting the relationship between research on crime and Artificial Intelligence (AI). Dazzled by the increasing availability of digital data and by the hype around the research and practice in machine intelligence virtually across every scientific field, readers would easily fall into the novelty trap, proving the effectiveness of such marketing strategy of scientific communication, not necessarily leading to more lenient evaluations of my work but quite certainly achieving a higher level of attractiveness.
Yet, as tempting as this might be, the truth is different. In fact, the relationship between machine learning and crime research is not novel at all. And the same goes for the broader relationship between AI and the social sciences. Although many papers, reports, contributions to the fields of criminology or crime research have been pictured as “novel” precisely because they rely on the use of machine learning or similar approaches from the AI literature, this “novelty” narrative rests on a – deliberated or not – fabrication of history.
Of course, elements of novelty emerge, including for instance in the type of data that are used or the specific algorithmic approaches that are tested. However, the relationship between these two areas has now quite a long history. Differences between the present and the past as well as important developments in this relationship exist, but dismissing the epistemological discussion around the ways in which crime research and AI are linked by invoking an alleged “novelty” oversimplifies a scientific process that has longstanding roots.
Acknowledging these roots is essential not only for mere historical and chronological reporting, giving credit to the reflections of pioneers that moved the first steps into this line of inquiry. It is also critical to contextualize the challenges we face today, the trends we are witnessing, and the perspectives we are unfolding. The “novelty” narrative obscures early debates in this area of research and severely limits our critical reasoning on the possible future scenarios resulting from the progressively tighter connection between criminology, crime research, and the nuances of AI.
The introduction to this book hence precisely starts with a call to reconnect with the decades-long past that precedes us, refusing the novelty narrative as a cheap marketing strategy to make our works more palatable. Novelty and innovation are essential aspects of science: they are the engine of progress, the forces leading humanity into the future, and they should be therefore invoked cautiously and, most importantly, after cognition and recognition of our past. It takes an appreciation of the process of knowledge-building started decades ago to discriminate advances from selling strategies, true innovation from rebranding, and real novelty from noise.
The immaturity of the dialogue between crime research and AI, an element that will be discussed in this book, also passes from here: from the inability (or lack of interest) to frame our present in perspective, moving beyond the impromptu logic of publication and dissemination of ideas for the sole purpose of being cited more, selling more book copies, acquiring more grants, pretending to be the elected representatives of a new wave of world-changing research. The “novelty narrative” pushes us all to think of a process that starts from square zero every time we work on an idea or a project, hence refusing a cumulative (and therefore comparative) approach to scientific discovery. However, criminology and crime research – and AI as well – require a cumulative and comparative approach to finally address and respond to pressing questions that go well beyond the abstract task of finding someone willing to publish our research. The “novelty narrative” works perfectly fine for our tenure-line goals and our run for academic prestige, but all the implications it poses severely impair our possibility to make a change in the real world. I will leave to the reader to decide whether this is an acceptable trade-off decision to make.
Courtesy by Routledge.