发表机构
The University of Western Australia; Johns Hopkins University（西澳大利亚大学; 约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文聚焦AI临床算法设计与医师责任规则，研究发现责任规则会引发AI使用差异，强制算法等准确率要求反而损害双方，为医疗AI监管提供了新视角。
AI 中文摘要
单一临床算法在不同患者群体中可能呈现出不均等的准确率，随着人工智能（AI）在临床决策中的普及，人们对这种差异的担忧日益加剧。针对这一问题，美国出台了一项责任规则：当医疗服务提供者因依赖存在差异的算法而导致临床决策出错时，需承担责任。本文研究此类责任考量如何改变两个方面：一是AI企业决定的、决定群体特定准确率的算法设计；二是医师在医疗服务中使用AI的决策。AI企业为两个患者群体设计算法，且提升弱势群体的准确率成本更高；医师作为最终负责的决策者，需在咨询AI带来的临床不确定性降低，与AI错误对弱势群体造成不成比例影响时的预期责任风险之间权衡。研究发现，该责任规则会引发AI使用的差异：医师可能整体减少AI使用，且在中等责任范围内，对弱势群体的AI依赖会降低，该影响呈非单调性；随着责任增加，医师对弱势群体的AI使用先下降，后因企业重新分配投资以缩小差异或转向等准确率设计而上升。强制要求算法在不同患者群体间准确率均等，反而可能同时损害两个群体，因为统一准确率要求会扭曲企业的投资激励与医师的均衡AI使用决策。
英文摘要
A single clinical algorithm can deliver unequal accuracy across patient groups, and concern about such disparity has grown as artificial intelligence (AI) spreads through clinical decision-making. In response, a liability rule introduced in the United States holds healthcare providers responsible when their reliance on disparate algorithms contributes to erroneous clinical decisions. We examine how such liability considerations reshape (i) an AI firm's algorithm design decisions that drive group-specific accuracy and (ii) a physician's decisions to use AI in healthcare delivery. The AI firm designs an algorithm for two patient groups, and improving accuracy for the disadvantaged group is more costly. The physician (who remains the accountable decision-maker) then decides whether to consult AI, weighing the reduction in clinical uncertainty against expected liability exposure when AI errors disproportionately affect the disadvantaged group. We find the liability rule can induce disparate use of AI: the physician may reduce AI use overall and, over an intermediate range of liability, rely on AI less for disadvantaged patients. The effect is non-monotone. As liability increases, the physician's use of AI for disadvantaged patients first declines, then rises as the firm reallocates investment toward reducing disparity or switches to an equal-accuracy design. Mandating equal algorithmic accuracy across patient groups can then inadvertently harm both groups, because a uniform accuracy requirement distorts the firm's investment incentives and the physician's equilibrium AI-use decisions.
Comments65 pages, 8 figures, 5 tables; includes online appendix (pp. 51-65)
