When Does Regulation by Insurance Work? The Case of Frontier AI
Cristian Trout

TL;DR
This paper develops a framework to evaluate when insurance can effectively regulate risky industries, applying it to frontier AI to identify conditions for successful regulation and policy interventions.
Contribution
It introduces a principled framework for assessing insurance as a regulatory tool, considering market distortions and applying it to frontier AI to identify regulatory potential.
Findings
Insurance can serve as an effective regulation in certain risky contexts.
Frontier AI shows significant regulatory potential through insurance, with policy design being crucial.
Market distortions influence the effectiveness of insurance-based regulation.
Abstract
No one doubts the utility of insurance for its ability to spread risk or streamline claims management; much debated is when and how insurance uptake can improve welfare by reducing harm, despite moral hazard. Proponents and dissenters of "regulation by insurance" have now documented a number of cases of insurers succeeding or failing to have such a net regulatory effect (in contrast with a net hazard effect). Collecting these examples together and drawing on an extensive economics literature, this Article develops a principled framework for evaluating insurance uptake's effect in a given context. The presence of certain distortions - including judgment-proofness, competitive dynamics, and behavioral biases - creates potential for a net regulatory effect. How much of that potential gets realized then depends on the type of policyholder, type of risk, type of insurer, and the structure of…
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Taxonomy
TopicsLaw, Economics, and Judicial Systems · Legal and Constitutional Studies · Decision-Making and Behavioral Economics
