Auction-Based Regulation for Artificial Intelligence
This academic paper addresses the critical lack of rigorous mathematical frameworks for regulating Artificial Intelligence (AI) amidst growing concerns over safety, bias, and legal liabilities. The authors propose a novel auction-based regulatory mechanism designed to incentivize enterprises to deploy compliant AI models and actively participate in the regulation process. By formulating AI regulation as an all-pay auction, the system allows regulators to enforce compliance thresholds while rewarding models that demonstrate higher compliance levels than their peers. The study derives Nash Equilibria to prove that rational agents are motivated to submit models exceeding prescribed standards. Empirical results indicate that this auction-based approach significantly outperforms traditional frameworks that merely impose minimum compliance standards. Specifically, the proposed mechanism boosts compliance rates by 20% and participation rates by 15%. This research offers a potential solution for regulators struggling to keep pace with rapid AI deployment, providing a structured, game-theoretic approach to enhancing AI safety and accountability through economic incentives rather than solely punitive measures.
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Auction-Based Regulation for Artificial Intelligence
This academic paper addresses the critical lack of rigorous mathematical frameworks for regulating Artificial Intelligence (AI) amidst growing concerns over safety, bias, and legal liabilities. The authors propose a novel auction-based regulatory mechanism designed to incentivize enterprises to deploy compliant AI models and actively participate in the regulation process. By formulating AI regulation as an all-pay auction, the system allows regulators to enforce compliance thresholds while rewarding models that demonstrate higher compliance levels than their peers. The study derives Nash Equilibria to prove that rational agents are motivated to submit models exceeding prescribed standards. Empirical results indicate that this auction-based approach significantly outperforms traditional frameworks that merely impose minimum compliance standards. Specifically, the proposed mechanism boosts compliance rates by 20% and participation rates by 15%. This research offers a potential solution for regulators struggling to keep pace with rapid AI deployment, providing a structured, game-theoretic approach to enhancing AI safety and accountability through economic incentives rather than solely punitive measures.
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