Artificial Intelligence and Criminology

Abstract

Machine learning is gaining momentum in criminology, and the unprecedented transformative power of generative artificial intelligence (AI) is poised to affect the discipline even more, perhaps in ways we currently struggle to imagine. In light of the growing role that AI plays in our field and in science overall, this article pursues three objectives. First, I trace the historical roots of AI in criminology and survey current trends to take stock of research in this area. Second, I outline five directions for future research that I deem strategic for criminology as a whole, surpassing the longstanding schism between quantitative and qualitative scholarship: going beyond the idea that AI solely works for prediction or forecasting, building AI literacy, fostering transdisciplinary collaboration, encouraging systematic and comparative algorithmic evaluation, embracing agentic AI, and raising open science standards. Third, I propose a fundamental shift in perspective for how we discuss AI in our discipline. As autonomous AI agents proliferate and interact with minimal human oversight, we must move beyond treating AI as a passive tool. I argue for developing a criminology of machines—a framework examining how artificial agents may themselves become channels or perpetrators of criminal behavior.