Optimal Medical Liability for AI

Alex Chan

No 35321, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: I study medical liability when artificial intelligence acts as a doctor rather than as a passive clinical tool. The central object is the legally usable medical record: the inputs, logs, warnings, prescriptions, follow-up instructions, and outcomes on which courts, contracts, insurers, and regulators can condition responsibility. I show that AI medical liability is an institutional design problem under imperfect legal information. If the record separates AI-controllable error from patient nonadherence and natural disease progression, high-powered AI-fault liability implements the standard accident-law ideal. If the record is coarse, the first best may be infeasible: the same transfer that disciplines the AI also insures the patient's hidden action. With joint causation, the relevant object is a marginal-responsibility score rather than a posterior cause label. I characterize the feasible set of liability incentives generated by the record and show when the optimal rule is no liability, strict liability, negligence, a safe harbor, comparative fault, or a continuous warranty. I then study algorithmic defensive design, through which AI developers can design not only medical recommendations but also the record on which future liability depends. Adoption, learning, enterprise liability, insurance, no-fault compensation, and regulation enter as ways to change the record, the liable entity, or the financing of compensation. The framework yields conditional implications rather than a one-size-fits-all rule.

JEL-codes: D47 D82 D86 D9 G22 I1 I13 I18 K12 K13 L51 (search for similar items in EconPapers)
Date: 2026-06
New Economics Papers: this item is included in nep-ain and nep-law
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