Insuring Uninsurable Risks from AI: The State as Insurer of Last Resort

Cristian Trout
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Abstract

Many experts believe that AI systems will sooner or later pose uninsurable risks, including existential risks. This creates an extreme judgment-proof problem: few if any parties can be held accountable ex post in the event of such a catastrophe. This paper proposes a novel solution: a government-provided, mandatory indemnification program for AI developers. The program uses risk-priced indemnity fees to induce socially optimal levels of care. Risk-estimates are determined by surveying experts, including indemnified developers. The Bayesian Truth Serum mechanism is employed to incent honest and effortful responses. Compared to alternatives, this approach arguably better leverages all private information, and provides a clearer signal to indemnified developers regarding what risks they must mitigate to lower their fees. It's recommended that collected fees be used to help fund the safety research developers need, employing a fund matching mechanism (Quadratic Financing) to induce an optimal supply of this public good. Under Quadratic Financing, safety research projects would compete for private contributions from developers, signaling how much each is to be supplemented with public funds.
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为人工智能无法保险的风险提供保险:国家作为最后的保险人
许多专家认为,人工智能系统迟早会带来不可预测的风险,包括生存风险。这就产生了一个极端的 "判断-证明"(judgment-proofproblem)问题:如果发生这样的灾难,几乎没有任何一方可以事后承担责任。本文提出了一种新颖的解决方案:由政府为人工智能开发者提供强制性赔偿计划。该计划利用风险定价的赔偿费来诱导社会最优的护理水平。风险估计值通过调查专家(包括被赔偿的开发者)来确定。采用贝叶斯真理血清机制来激励诚实和努力的回答。与其他方法相比,这种方法可以说更好地利用了所有私人信息,并为受补偿开发商提供了一个更明确的信号,让他们知道必须降低哪些风险才能降低费用。建议将收取的费用用于资助开发者所需的安全研究,采用资金匹配机制(二次融资)来实现这种公共产品的最优供给。在四次方融资机制下,安全研究项目将与开发商竞争私人捐款,以表明公共资金对每个项目的补充程度。
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