为高风险AI事故设计责任与保险机制，借鉴核电行业经验
Liability and Insurance for Catastrophic Losses: the Nuclear Power Precedent and Lessons for AI
- 建议对前沿AI开发者施加严格第三方责任，针对可能引发灾难的事件
- 强制保险可解决开发者无力赔偿问题，并促进风险建模与预防
- 适合政策制定者与AI安全研究者参考，关注系统性风险管控
随着人工智能系统自主性与能力提升，专家警告其可能导致灾难性损失。本文借鉴核能行业的成功先例，主张对前沿AI模型开发者施加有限、严格且排他性的第三方责任，以应对可能导致或极易引发灾难性损失的‘关键AI事件’（CAIOs）。建议强制投保CAIO责任险，以克服开发者‘无赔偿能力’的问题，缓解赢家诅咒效应，并利用保险公司的准监管能力。基于理论分析和核能领域的观察，保险公司预计将开展因果风险建模、持续监控、推动更严格监管及提供损失预防指导，以应对人工智能带来的重尾风险。尽管不能替代监管，但明确的责任划分与强制保险有助于高效配置资源用于风险建模与安全设计，为未来监管提供支持。
原文摘要 · Abstract (English)
As AI systems become more autonomous and capable, experts warn of them potentially causing catastrophic losses. Drawing on the successful precedent set by the nuclear power industry, this paper argues that developers of frontier AI models should be assigned limited, strict, and exclusive third party liability for harms resulting from Critical AI Occurrences (CAIOs) - events that cause or easily could have caused catastrophic losses. Mandatory insurance for CAIO liability is recommended to overcome developers' judgment-proofness, mitigate winner's curse dynamics, and leverage insurers' quasi-regulatory abilities. Based on theoretical arguments and observations from the analogous nuclear power context, insurers are expected to engage in a mix of causal risk-modeling, monitoring, lobbying for stricter regulation, and providing loss prevention guidance in the context of insuring against heavy-tail risks from AI. While not a substitute for regulation, clear liability assignment and mandatory insurance can help efficiently allocate resources to risk-modeling and safe design, facilitating future regulatory efforts.
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