AI Governance through Markets

TL;DR

This paper argues that market-based governance—via insurance, auditing, procurement standards, and due diligence—should complement traditional AI regulation to reduce risk and accelerate safe deployment. It formalizes AI risk using a risk-adjusted value framework and demonstrates how four market mechanisms can distribute risk, reveal information, and guide capital toward safer AI. Through case studies in real estate insurance, Zoom, NASA procurement, and BP, it illustrates how these market forces influence corporate behavior and risk management. The work advocates standardized AI risk disclosures as the foundation for coordinated market judgments, highlighting the potential to unlock substantial funding for safety research while stressing careful design to avoid stifling innovation.

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.

Figures (4)

• Figure 1: 4 AI market governance mechanisms.
• Figure 2: Figure 1. D Scatter Plot of RAV as a Function of $E(X)$, $\sigma(X)$, and $\lambda$. This visualisation illustrates how varying each parameter affects the RAV, with warmer colours indicating higher risk-adjusted values.
• Figure 3: Effect of Information Asymmetry on Risk Aversion Coefficient $\lambda(I)$. The graph illustrates how $\lambda(I)$ varies with information asymmetry $I$ for $s_\lambda = -0.5$ (Underestimation of Risk Aversion) and $s_\lambda = +0.5$ (Overestimation of Risk Aversion).
• Figure 4: The 6 macro stages of the large AI model production line. This is a simplified overview. Other stages are described in the article text. This is not a comprehensive overview of large AI model development.