Abstract
Research on artificial intelligence (AI) applications has spread over many scientific disciplines. Scientists have tested the power of intelligent algorithms developed to predict (or learn from) natural, physical and social phenomena. This also applies to crime-related research problems. Nonetheless, studies that map the current state of the art at the intersection between AI and crime are lacking. What are the current research trends in terms of topics in this area? What is the structure of scientific collaboration when considering works investigating criminal issues using machine learning, deep learning, and AI in general? What are the most active countries in this specific scientific sphere? Using data retrieved from the Scopus database, this work quantitatively analyzes 692 published works at the intersection between AI and crime employing network science to respond to these questions. Results show that researchers are mainly focusing on cyber-related criminal topics and that relevant themes such as algorithmic discrimination, fairness, and ethics are considerably overlooked. Furthermore, data highlight the extremely disconnected structure of co-authorship networks. Such disconnectedness may represent a substantial obstacle to a more solid community of scientists interested in these topics. Additionally, the graph of scientific collaboration indicates that countries that are more prone to engage in international partnerships are generally less central in the network. This means that scholars working in highly productive countries (e.g. the United States, China) tend to mostly collaborate domestically. Finally, current issues and future developments within this scientific area are also discussed.
Highlights
The last two decades have witnessed a growing interest of scholars coming from natural, physical, and mathematical sciences in social science problems
Mathematical and statistical modeling have widespread across multiple disciplines that focus on the study of human beings and societies and that have traditionally been marked by qualitative research
Besides economics, which is traditionally more receptive in adopting quantitative approaches, mathematics and statistics have infiltrated many other disciplines falling under the broad category of “social sciences”, including sociology, political science, and criminology [2, 7, 25, 35, 50, 57, 64, 67]
Summary
The last two decades have witnessed a growing interest of scholars coming from natural, physical, and mathematical sciences in social science problems. Despite the relevant debates that have emerged regarding two areas of application of AI systems, namely criminal justice and policing [6, 65], the literature lacks an assessment of the research production integrating intelligent algorithms and the analysis of offenders and criminal behaviors. In light of these considerations, this work proposes to map the extant literature using Scopus, a database containing over 69 million abstract and citation records of peer-reviewed literature. In the "Where to, now? Discussion and future developments" section, considerations derived from the analyses will be drawn in the attempt to better picture this strand of research and to define its current issues and potential future pathways
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