[2603.05175] Incentive Aware AI Regulations: A Credal Characterisation

Computer Science > Machine Learning arXiv:2603.05175 (cs) [Submitted on 5 Mar 2026] Title:Incentive Aware AI Regulations: A Credal Characterisation Authors:Anurag Singh, Julian Rodemann, Rajeev Verma, Siu Lun Chau, Krikamol Muandet View a PDF of the paper titled Incentive Aware AI Regulations: A Credal Characterisation, by Anurag Singh and 4 other authors View PDF HTML (experimental) 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 fea...