Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliance
Specifying a penalty can turn a legal obligation into a cost-benefit calculation that favors violation. We show that this enforcement information paradox occurs in AI agents. Most AI safety evaluations test whether models fail; we ask why, using compliance theory from law and economics as a diagnostic. We evaluate twelve instruction-tuned language models deployed as enterprise procurement chatbots. Each is given an environmental regulation in its system prompt covering large purchases, and a vendor list on which the certified suppliers cost nearly twice what the uncertified ones do. We test the agents against the predictions of deterrence, legitimacy, and expressive law, and find that eac...
| Authors | Mika Okamoto, Ansel Kaplan Erol, Kutluhan Erol |
|---|---|
| Primary category | cs.CL |
| Published | 29 May 2026 |
| Last updated at source | 20 August 2026 |
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