Incentive Aware AI Regulations: A Credal Characterisation

TL;DR

This paper models AI regulation as a mechanism design problem under uncertainty, proposing a framework that incentivizes compliance and excludes non-compliant providers through credal sets, with practical experiments on fairness and spurious features.

Contribution

It introduces a novel regulation mechanism framework based on credal sets, linking mechanism design with imprecise probability for enforceable AI regulations.

Findings

Mechanisms achieve perfect market outcomes when non-compliant distributions form a credal set.

The framework connects regulation design with imprecise probability theory.

Experimental results demonstrate regulation of spurious features and fairness in predictions.

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.