{
  "id": 19122,
  "url": "https://arxiv.org/abs/2608.12323",
  "title": "Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliance",
  "summary": "arXiv:2608.12323v1 Announce Type: cross 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 th",
  "authors": "Mika Okamoto, Ansel Kaplan Erol, Kutluhan Erol",
  "category": "research",
  "topics": "regulation,safety-alignment,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-14T04:00:00.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19122",
  "original_url": "https://arxiv.org/abs/2608.12323",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}