In an era of "moving fast and breaking things", regulators have moved slowly to pick up the safety, bias, and legal debris left in the wake of broken Artificial Intelligence (AI) deployment. While there is much-warranted discussion about how to address the safety, bias, and legal woes of state-of-the-art AI models, rigorous and realistic mathematical frameworks to regulate AI are lacking. Our paper addresses this challenge, proposing an auction-based regulatory mechanism that provably incentivizes agents (i) to deploy compliant models and (ii) to participate in the regulation process. We formulate AI regulation as an all-pay auction where enterprises submit models for approval. The regulator enforces compliance thresholds and further rewards models exhibiting higher compliance than their peers. We derive Nash Equilibria demonstrating that rational agents will submit models exceeding the prescribed compliance threshold. Empirical results show that our regulatory auction boosts compliance rates by 20% and participation rates by 15% compared to baseline regulatory mechanisms, outperforming simpler frameworks that merely impose minimum compliance standards.

翻译：在"快速行动、打破常规"的时代，监管机构行动迟缓，未能及时清理因人工智能(AI)部署不当而遗留的安全、偏见和法律碎片。尽管针对如何解决先进AI模型的安全、偏见和法律困境已有诸多必要讨论，但缺乏严谨且符合实际的数学框架来监管AI。本文针对这一挑战，提出了一种基于拍卖的监管机制，该机制可证明地激励智能体(i)部署合规模型，并(ii)参与监管过程。我们将AI监管形式化为一个全支付拍卖，其中企业提交模型以供审批。监管机构设定合规阈值，并对表现出比同行更高合规性的模型给予奖励。我们推导出纳什均衡，证明理性智能体将提交超过规定合规阈值的模型。实证结果表明，与基准监管机制相比，我们的监管拍卖将合规率提高了20%，参与率提高了15%，优于仅设定最低合规标准的简单框架。