报告题目：Optimal Liability Design for Medical AI

报 告 人：毛睿

报告时间：2026年4月28日（星期二），9:00-10:30

报告地点：腾讯会议（635-910-146）

主办单位：东北财经大学现代供应链管理研究院

【报告人简介】

毛睿，南京大学管理科学与工程专业博士研究生，美国田纳西大学联合培养博士研究生，曾在香港理工大学商学院访问交流。研究方向包括供应链管理、运营管理与市场营销交叉领域，以及人工智能驱动的医疗运营管理等。主持国家自然科学基金青年学生基础研究项目1项。以第一作者在Naval Research Logistics、IEEE Transactions on Engineering Management等期刊发表论文，其中发表于NRL期刊上的论文入选Wiley出版社“Top Viewed Article”。另有多篇论文正在IISE Transactions、Manufacturing & Service Operations Management等期刊返修或审稿中。

【摘要】

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 treatment, or following an imperfect AI recommendation. Our analysis yields several novel insights. First, we show that the optimal mechanism under asymmetric information is surprisingly simple: a uniform, one-size-fits-all liability level for all physician types who deviate from the standard of care. Despite physician heterogeneity, this simple policy often achieves the full-information first-best outcome, particularly when standard care is reliable or AI is highly accurate. Second, the relationship between AI accuracy and optimal liability is non-monotonic. Contrary to common intuition, better AI does not always imply more relaxed liability. As AI accuracy increases, the optimal liability either decreases monotonically or follows an inverted-U pattern, depending on the uncertainty of the standard treatment. Third, asymmetric information does not universally reduce social welfare. Welfare loss arises only when standard care is unreliable and AI accuracy is too low; even then, its magnitude follows an inverted U-shape, initially increasing as AI complicates the regulatory problem, but declining as more accurate AI helps mitigate it. Finally, we find that information asymmetry is a double-edged sword in the presence of AI, and greater transparency does not benefit all stakeholders equally.