# A Criminology of Machines arXiv: https://arxiv.org/abs/2511.02895 ยท HTML: https://mlsanity.com/paper/2511.02895/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 51 of its 174 references that are in the map. - First posted on arXiv 4 Nov 2025. ## Builds on (most cited first, 30 of 51) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016) - [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023) - [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023) - [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020) - [Persona Vectors: Monitoring and Controlling Character Traits in Language Models](https://mlsanity.com/paper/2507.21509/) (2025) - [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024) - [The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://mlsanity.com/paper/2506.06941/) (2025) - [A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration](https://mlsanity.com/paper/2310.02170/) (2023) - [Small Language Models are the Future of Agentic AI](https://mlsanity.com/paper/2506.02153/) (2025) - [A Survey of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2312.14925/) (2023) - [Large Language Model Alignment: A Survey](https://mlsanity.com/paper/2309.15025/) (2023) - [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023) - [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020) - [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025) - [AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways](https://mlsanity.com/paper/2406.02630/) (2024) - [Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia](https://mlsanity.com/paper/2312.03664/) (2023) - [Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents](https://mlsanity.com/paper/2404.16698/) (2024) - [Emergent social conventions and collective bias in LLM populations](https://mlsanity.com/paper/2410.08948/) (2024) - [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017) - [LLM Social Simulations Are a Promising Research Method](https://mlsanity.com/paper/2504.02234/) (2025) - [Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?](https://mlsanity.com/paper/2502.15657/) (2025) - [Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents](https://mlsanity.com/paper/2505.02077/) (2025) - [Strong Model Collapse](https://mlsanity.com/paper/2410.04840/) (2024) - [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020) - [Escalation Risks from Language Models in Military and Diplomatic Decision-Making](https://mlsanity.com/paper/2401.03408/) (2024) - [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024) - [Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models](https://mlsanity.com/paper/2504.04717/) (2025) - [Current state of LLM Risks and AI Guardrails](https://mlsanity.com/paper/2406.12934/) (2024) - [Fully Autonomous AI Agents Should Not be Developed](https://mlsanity.com/paper/2502.02649/) (2025) ## Built on by (most cited first, 1 of 1) - [Generative AI collective behavior needs an interactionist paradigm](https://mlsanity.com/paper/2601.10567/) (2026) ## Abstract (arXiv) 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.