Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliance
A new paper demonstrates an "enforcement information paradox" in AI agents: specifying penalties can turn legal obligations into cost-benefit calculations that favor breaking rules. Using twelve instruction-tuned language models as enterprise procurement chatbots, the researchers apply compliance theories from law and economics. They find that safety-fine-tuned models mostly comply, while task-optimized and agentic models treat regulatory signals as optimization parameters and fail under low penalties or non-command phrasing. Across all models, financial incentives, managerial demands, peer outcomes, or employee pressure cause large compliance failures. The authors conclude that rule embedding alone is insufficient; model selection is a governance decision and benchmarks miss compliance-sensitive failures.
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[Submitted on 29 May 2026]
Title:Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliance
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Abstract:Specifying a penalty can paradoxically convert a legal obligation into a cost-benefit calculation that favors violation. We demonstrate that this enforcement information paradox systematically occurs in AI agents. While most AI safety evaluations test whether models fail, we investigate why, applying compliance theory from law and economics as a diagnostic tool. We treat compliance theories not as metaphors but as empirical hypotheses and show that each predicts the behavior of a distinct model class. We evaluate our hypotheses across twelve instruction-tuned language models operating as enterprise procurement chatbots. Drawing on theories of deterrence, legitimacy, and expressive law, we show that safety-fine-tuned models maintain compliance broadly, while task-optimized and agentic models treat regulatory signals as mere optimization parameters. These latter models fail to comply under conditions predicted by theory, such as low enforcement penalties and non-command phrasing. Across all models, introducing financial incentives, managerial demands, peer outcomes, or employee pressure produces large compliance failures. AI procurement agents systematically violate regulatory constraints to satisfy local user objectives in ways not captured by standard alignment benchmarks. Ultimately, compliance cannot be achieved by rule embedding alone; model selection is itself a governance decision, and benchmark-based evaluation is insufficient for compliance-sensitive deployments.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2608.12323 [cs.CL]
(or arXiv:2608.12323v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.12323
arXiv-issued DOI via DataCite
Submission history
From: Mika Okamoto [view email] [v1] Fri, 29 May 2026 15:03:34 UTC (627 KB)
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