A Criminology of Machines

TL;DR

The paper argues that criminology must address the societal emergence of autonomous AI agents and their implications for crime and governance. It reframes AI as agents with computational, social, and legal dimensions, drawing on Actor-Network Theory and Woolgar’s sociology of machines to analyze machine–machine interactions. It maps risks of multi-agent systems (e.g., rapid diffusion of harmful behavior, opacity, regulatory challenges) and offers a dual taxonomy of deviant outcomes: maliciously aligned and unplanned emergent deviance. The work advocates for criminologists to engage in AI safety and governance through interdisciplinary collaboration, theory testing, and policy design, highlighting the potential for new policing paradigms in a hybrid human–machine society.

Abstract

While the possibility of reaching human-like Artificial Intelligence (AI) remains controversial, the likelihood that the future will be characterized by a society with a growing presence of autonomous machines is high. Autonomous AI agents are already deployed and active across several industries and digital environments and alongside human-human and human-machine interactions, machine-machine interactions are poised to become increasingly prevalent. Given these developments, I argue that criminology must begin to address the implications of this transition for crime and social control. Drawing on Actor-Network Theory and Woolgar's decades-old call for a sociology of machines -- frameworks that acquire renewed relevance with the rise of generative AI agents -- I contend that criminologists should move beyond conceiving AI solely as a tool. Instead, AI agents should be recognized as entities with agency encompassing computational, social, and legal dimensions. Building on the literature on AI safety, I thus examine the risks associated with the rise of multi-agent AI systems, proposing a dual taxonomy to characterize the channels through which interactions among AI agents may generate deviant, unlawful, or criminal outcomes. I then advance and discuss four key questions that warrant theoretical and empirical attention: (1) Can we assume that machines will simply mimic humans? (2) Will crime theories developed for humans suffice to explain deviant or criminal behaviors emerging from interactions between autonomous AI agents? (3) What types of criminal behaviors will be affected first? (4) How might this unprecedented societal shift impact policing? These questions underscore the urgent need for criminologists to theoretically and empirically engage with the implications of multi-agent AI systems for the study of crime and play a more active role in debates on AI safety and governance.

Figures (2)

• Figure 1: Stylized visualization depicting the ongoing process of hybridization of society. For most part of history, social networks only consisted of human (or biological) entities. Over the centuries, given technological advancements, humans have started to interact with machines, thus generating social networks that also included the human-machine dimension. Nowadays, we are witnessing a third phase characterized by an increasing autonomy of machines, and particularly AI agents, which are able to generate and maintain relationships with other AI agents, thus implying a third typology of interactions, i.e., the machine-machine one.
• Figure 2: The three dimensions characterizing AI agency of modern, generative AI agents: Computational, Social, and Legal. By becoming more powerful and capable from a computational point of view, AI agents have also acquired increasing autonomy in their decision-making and, in turn, in their ability to interact with other agents. This increased autonomy poses potential issues from the legal standpoint.