AI Governance through Markets

2025-01-29

AI Summary

This paper addresses the dual challenges of AI-driven security risks and economic asymmetries arising from rapid AI advancement, proposing market-based governance as a core solution. It examines four emerging institutional mechanisms—AI insurance, third-party auditing, procurement standards, and due diligence requirements—designed to internalize AI risks as quantifiable financial liabilities, thereby optimizing capital allocation and incentivizing responsible AI development. The study introduces the first systematic framework for AI market governance, integrating standardized risk disclosure with differentiated market incentives to overcome the limitations of command-and-control regulation in adaptability and incentive compatibility. Drawing on institutional economics, financial engineering, and AI safety assessment, it emphasizes actionable infrastructure design. Empirical analysis demonstrates the efficacy of these mechanisms in risk pricing and behavioral alignment. The work offers policymakers, economists, and AI researchers a theoretically rigorous yet operationally viable paradigm for coordinated, market-informed governance.

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
• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Technology governance, policy, and regulations

Abstract

This paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. While current governance approaches have predominantly focused on regulation, we contend that market-based mechanisms offer effective incentives for responsible AI development. We examine four emerging vectors of market governance: insurance, auditing, procurement, and due diligence, demonstrating how these mechanisms can affirm the relationship between AI risk and financial risk while addressing capital allocation inefficiencies. While we do not claim that market forces alone can adequately protect societal interests, we maintain that standardised AI disclosures and market mechanisms can create powerful incentives for safe and responsible AI development. This paper urges regulators, economists, and machine learning researchers to investigate and implement market-based approaches to AI governance.

Problem

• Artificial Intelligence Governance
• Market Mechanism
• Economic Asymmetry

Innovation

• Market-Based Management
• AI Governance
• Economic Risk Alignment