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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.
List of contents
Chapter 1: The "Novelty Narrative": An Unorthodox Introduction
Chapter 2: A Collective Journey: A Short Overview on Artificial Intelligence
Chapter 3: Criminology at the Crossroads? Computational Perspectives
Chapter 4: To Reframe and Reform: Increasing the Positive Social Impact of Algorithmic Applications in Research on Crime
Chapter 5: Causal Inference in Criminology and Crime Research and the Promises of Machine Learning
Chapter 6: Concluding Remarks
About the author
Gian Maria Campedelli is a Postdoctoral Research Fellow in Computational Sociology and Criminology at the University of Trento, Italy. In 2020, he earned a PhD in Criminology from Catholic University in Milan, Italy. From 2016 to 2019 he worked as a researcher at Transcrime, the Joint Research Center on Transnational Crime of Catholic University, University of Bologna, and University of Perugia. In 2018 he was also a visiting research scholar in the School of Computer Science at Carnegie Mellon University, in Pittsburgh, the United States. His research addresses the development and application of computational methods – especially machine learning and complex networks – to the study of criminal and social phenomena, with a specific focus on organized crime, violence, and terrorism.
Summary
Machine Learning for Criminology and Crime Research reviews the roots of the intersection between machine learning, Artificial Intelligence, 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.
