用拍卖机制激励企业主动合规并参与监管，提升AI安全治理效果。
Auction-Based Regulation for Artificial Intelligence
- 设计全支付拍卖模型，让企业为模型审批竞价并获合规奖励。
- 实验证明合规率提升20%，参与度提高15%，优于简单标准监管。
- 适合关注AI治理、政策设计与激励机制的研究者和决策者。
在‘快速迭代、频繁出错’的AI发展背景下，监管滞后于技术带来的安全、偏见与法律问题。尽管对前沿AI模型的安全性、偏见与法律风险讨论广泛，但缺乏严谨且现实的数学监管框架。本文提出一种基于拍卖的监管机制，可严格激励企业（1）部署符合规范的模型，（2）积极参与监管过程。我们将AI监管建模为全支付拍卖，企业提交模型以获取审批，监管机构设定合规阈值，并对表现优于同行的模型给予额外奖励。我们推导出纳什均衡，证明理性企业将提交超过最低合规要求的模型。实验表明，相较于基础监管机制，该方法使合规率提升20%，参与率提高15%，优于仅设最低标准的简单框架。
原文摘要 · Abstract (English)
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.
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