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<p>Artificial intelligence (AI) has transformed modern society, influencing industries ranging from business and education to law enforcement and governance. However, the rapid advancement of AI has also introduced new risks and challenges in the criminal domain. This study conducts a comprehensive bibliometric and conceptual analysis of global research on AI and crime between 2020 and 2025, drawing on 510 Scopus-indexed publications. Using VOSviewer, the study visualizes collaboration patterns, citation structures, and thematic clusters to map the intellectual landscape of this interdisciplinary field. The results reveal four dominant research layers: AI as a tool for crime, AI as a source of bias, AI as a law enforcement instrument, and AI ethics and governance. Findings indicate a growing convergence between technological innovation and ethical accountability, with increasing attention to algorithmic fairness, transparency, and legal responsibility. The conceptual model—the AI Criminal Ecosystem—captures how AI capabilities, malicious use, forensic responses, and governance interact dynamically. This study highlights the evolution of AI-crime research toward an integrated and ethically conscious discipline and provides directions for future inquiry into policy, regulation, and justice system applications.</p>

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Artificial intelligence and crime research landscape a bibliometric and conceptual analysis from 2020 to 2025

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月17日

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  • Zongwen Xia,
  • Chirayut Khamboon

摘要

Artificial intelligence (AI) has transformed modern society, influencing industries ranging from business and education to law enforcement and governance. However, the rapid advancement of AI has also introduced new risks and challenges in the criminal domain. This study conducts a comprehensive bibliometric and conceptual analysis of global research on AI and crime between 2020 and 2025, drawing on 510 Scopus-indexed publications. Using VOSviewer, the study visualizes collaboration patterns, citation structures, and thematic clusters to map the intellectual landscape of this interdisciplinary field. The results reveal four dominant research layers: AI as a tool for crime, AI as a source of bias, AI as a law enforcement instrument, and AI ethics and governance. Findings indicate a growing convergence between technological innovation and ethical accountability, with increasing attention to algorithmic fairness, transparency, and legal responsibility. The conceptual model—the AI Criminal Ecosystem—captures how AI capabilities, malicious use, forensic responses, and governance interact dynamically. This study highlights the evolution of AI-crime research toward an integrated and ethically conscious discipline and provides directions for future inquiry into policy, regulation, and justice system applications.