Auction-Based Regulation for Artificial Intelligence

2024-10-02

arXiv.org

AI Summary

Current AI regulation suffers from delayed responsiveness and a lack of rigorous mathematical foundations. Method: This paper proposes a novel regulatory paradigm grounded in an all-pay auction mechanism, modeling model approval as an incentive-compatible bidding process; it introduces Nash equilibrium analysis to AI regulation design for the first time, ensuring provably compliant incentives and voluntary participation. The mechanism dynamically links compliance thresholds to firms’ bids, thereby incentivizing submission of high-quality models. Contribution/Results: Empirical evaluation demonstrates that, compared to traditional minimum-standard regulation, the proposed framework increases compliance rates by 20% and firm participation rates by 15%. It overcomes the limitations of command-and-control approaches and establishes the first game-theoretic regulatory framework for AI governance that is both theoretically rigorous and practically implementable.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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 devices (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.

Problem

• Auction-based AI regulation
• Incentivizing compliant model deployment
• Boosting compliance and participation rates

Innovation

• Auction-based regulatory mechanism
• All-pay auction formulation
• Compliance and participation incentives