医疗人工智能的最优责任设计

中文摘要 AI 辅助

人工智能（AI）正越来越多地融入医疗决策，但其责任相关问题仍很复杂，尤其是当医生的诊断技能存在差异且其质量不可观测时。本文构建了一个委托代理模型，其中社会计划者设计医疗责任以监管一名拥有私人质量信息的医生，该医生可在标准治疗、基于个性化判断的治疗或遵循不完善的AI建议之间做出选择。我们的分析得出若干新颖见解：第一，我们证明，不对称信息下的最优机制出人意料地简单：对所有偏离护理标准的医生类型采用统一的、一刀切的责任水平。尽管医生存在异质性，这一简单政策往往能实现完全信息下的最优结果，尤其是当标准治疗可靠或AI高度准确时。第二，AI准确度与最优责任之间的关系是非单调的。与常见直觉相反，更好的AI并不总是意味着更宽松的责任。随着AI准确度提高，最优责任要么单调下降，要么遵循倒U型模式，具体取决于标准治疗的不确定性。第三，不对称信息并不总是降低社会福利。仅当标准治疗不可靠且AI准确度过低时才会出现福利损失；即便如此，其幅度也呈倒U型，最初随着AI使监管问题复杂化而增加，但随着更准确的AI帮助缓解该问题而下降。最后，我们发现，在AI存在的情况下，信息不对称是一把双刃剑，更高的透明度并不会平等地惠及所有利益相关者。

英文摘要

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.

发表机构

• Nanjing University（南京大学）
• University of Tennessee（田纳西大学）

机构由 AI 辅助整理，请以论文原文为准。