Algorithm Design and Physician Liability

A new liability rule in the United States holding healthcare providers responsible for reliance on disparate AI algorithms can reduce overall AI use and, over an intermediate range of liability, lead physicians to rely on AI less for disadvantaged patients, according to a study posted on arXiv (2608.13618v1). The study finds the effect is non-monotone: as liability increases, physician AI use for disadvantaged patients first declines, then rises as the AI firm reallocates investment toward reducing disparity or switches to an equal-accuracy design. Mandating equal algorithmic accuracy across patient groups can inadvertently harm both groups by distorting the firm's investment incentives and the physician's equilibrium AI-use decisions.

arXiv:2608.13618v1 Announce Type: new Abstract: 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.