Geistfeld on AI and Insurability

Mark Geistfeld has posted to SSRN Insurability and Liability for AI-Caused Harms. The abstract provides:

The opacity of AI decision-making has led many tort scholars to conclude that ordinarily it will be infeasible to prove negligence or defect-based forms of products liability for AI-caused harms. According to mainstream tort theory, this evidentiary hurdle justifies strict enterprise liability for commercial AI distributors. Fully internalizing injury costs within these business enterprises adequately incentivizes them to adopt reasonably safe practices while relying on their liability insurance policies to efficiently and fairly compensate accident victims. Mainstream theory, however, decisively biases the analysis in favor of strict enterprise liability by not accounting for how the expansion of liability would substantially increase the cost of compensating injuries through insurance mechanisms. A commercial tortfeasor’s liability insurance routinely indemnifies injuries of plaintiffs who are already insured under their own first-party policies, such as health insurance. All else being equal, this duplication of insurance coverage considerably increases the total cost of injury compensation for right-holders who ultimately pay for commercial liabilities through increases in product prices and the like. By massively expanding the scope of liability, strict enterprise liability would substantially increase the amount of duplicated coverage and drive up total insurance costs. Consequently, even if strict liability would reduce risk relative to a negligence regime due to problems of proof, right-holders would be disadvantaged if that safety benefit does not exceed their dramatically increased cost of insuring against injury. This largely overlooked insurance dimension of the tort problem shows why strict enterprise liability could harm the right-holders the liability rule is intended to protect. Accounting for the structural relation between insurability and liability is essential for formulating efficient and fair liability rules governing AI-caused harms.