Shujie Luan (Johns Hopkins University – Carey Business School), Shubhranshu Singh (Johns Hopkins University – Carey Business School), & Tinglong Dai (Johns Hopkins University – Carey Business School; Johns Hopkins University – Hopkins Business of Health Initiative) have posted Algorithmic Bias and Physician Liability on SSRN. Here is the abstract:
With the growing use of artificial intelligence (AI) in clinical decision-making, concerns about bias-manifested as differences in algorithmic accuracy across patient groups-have intensified. In response, the U.S. Centers for Medicare and Medicaid Services (CMS) has introduced a liability rule that penalizes healthcare providers who rely on biased algorithms that result in erroneous decisions. This paper examines the impact of this anti-bias liability rule on an AI firm’s development decision as well as a healthcare provider’s decision to use AI. The AI firm develops an algorithm that serves two patient groups, where achieving the same level of accuracy for the disadvantaged group is more costly. The provider then decides whether and how to use AI to make treatment decisions, balancing the reduction in clinical uncertainty against the risk of incurring anti-bias liability. We find the liability rule may induce biased use of AI: The provider may underuse AI overall and disproportionately disregard AI’s recommendations for disadvantaged patients. Interestingly, the effect of liability on AI use is non-monotone: as liability increases, the provider is first less likely to use AI for disadvantaged patients, but then more likely to rely on it. Furthermore, mandating equal algorithmic accuracy across patient groups may inadvertently harm all patients, in part because such mandates may lead to overusing AI for disadvantaged patients.
{Categories} _Category: Implications{/Categories}
{URL}https://lsolum.typepad.com/legaltheory/2024/12/luan-singh-dai-on-algorithmic-bias-and-physician-liability.html{/URL}
{Author}Lawrence Solum{/Author}
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{Source}Implications{/Source}
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