{
  "id": 16202,
  "url": "https://arxiv.org/abs/2608.03114",
  "title": "Optimal Liability Design for Medical AI",
  "summary": "arXiv:2608.03114v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly integrated into medical decision-making, yet its liability implications remain complex, particularly when physicians differ in diagnostic skills and their quality is unobservable. This paper develops a principal-agent model in which a social planner designs medical liability to regulate a physician with private quality information who chooses between a standard treatment, a personalized judgment-based",
  "authors": "Rui Mao, Tingliang Huang, Houcai Shen",
  "category": "research",
  "topics": "regulation,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T04:00:00.000Z",
  "fetched_at": "2026-08-05T05:10:44.550Z",
  "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/16202",
  "original_url": "https://arxiv.org/abs/2608.03114",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}