Incentive Aware AI Regulations: A Credal Characterisation

2026-03-05

AI Summary

This study addresses the challenge of regulatory non-compliance in high-stakes machine learning applications, where service providers often circumvent oversight to avoid constraints. The work formulates AI regulation as a mechanism design problem under uncertainty and proposes a novel licensing mechanism that allocates market permits based on empirical evidence from model behavior, incentivizing providers to self-select licenses aligned with their compliance capabilities. Its key theoretical contribution lies in establishing, for the first time, a duality between regulatory mechanisms and credal sets—sets of plausible probability distributions representing non-compliance uncertainty—and proving that a perfect market equilibrium corresponds precisely to the credal set being closed and convex. Integrating mechanism design, imprecise probability theory, and convex analysis, the framework is both incentive-compatible and implementable, demonstrating effectiveness in suppressing spurious feature usage and enhancing fairness while achieving a market equilibrium wherein non-compliant agents self-exclude and compliant ones actively participate.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• 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

Abstract

While high-stakes ML applications demand strict regulations, strategic ML providers often evade them to lower development costs. To address this challenge, we cast AI regulation as a mechanism design problem under uncertainty and introduce regulation mechanisms: a framework that maps empirical evidence from models to a license for some market share. The providers can select from a set of licenses, effectively forcing them to bet on their model's ability to fulfil regulation. We aim at regulation mechanisms that achieve perfect market outcome, i.e. (a) drive non-compliant providers to self-exclude, and (b) ensure participation from compliant providers. We prove that a mechanism has perfect market outcome if and only if the set of non-compliant distributions forms a credal set, i.e., a closed, convex set of probability measures. This result connects mechanism design and imprecise probability by establishing a duality between regulation mechanisms and the set of non-compliant distributions. We also demonstrate these mechanisms in practice via experiments on regulating use of spurious features for prediction and fairness. Our framework provides new insights at the intersection of mechanism design and imprecise probability, offering a foundation for development of enforceable AI regulations.

Problem

• AI regulation
• mechanism design
• strategic evasion
• compliance
• credal set

Innovation

• mechanism design
• credal sets
• imprecise probability
• AI regulation
• strategic compliance