2019/12/23 by Gian Maria Campedelli · 1 voice · 1 citation
Computer Science · Social Sciences · #Crime Patterns and Interventions #Cybercrime and Law Enforcement Studies #Ethics and Social Impacts of AI #cs.CY #cs.DL #cs.LG
paper · pdf · doi:10.1007/s42001-020-00082-9
published as J Comput Soc Sc (2020) · 25 pages, 12 figures, pre-print (currently R&R in JCSS)
arxiv published 2019/12/23 · openalex created_date 2020/01/10 · arxiv created 2020/08/06 · openalex publication_date 2020/09/27 · arxiv updated 2020/11/12 · openalex updated_date 2026/08/01
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.