Incentive Aware AI Regulation

Anurag Singh, Julian Rodemann, Rajeev Verma, Siu Lun Chau, Krikamol Muandet

Keywords: AI regulation, Principal Agent Framework, Imprecise Probability

Abstract: The EU AI Act emphasizes the importance of differentiated safety requirements across classes of users. However, machine learning (ML) service providers may strategically under-enforce such requirements to reduce development costs or accelerate deployment. We study this phenomenon through the lens of a principal–agent model, where regulators act as principals enforcing risk-control obligations, while ML service providers act as agents with private incentives. A key challenge is that direct enforcement of safety constraints is often infeasible, since verification requires costly monitoring and statistical uncertainty may be exploited by strategic agents. To address this, we introduce incentive aware statistical protocols—rules tailored for the providers given their private costs, that translate observed model performance into enforceable outcomes, such as licensed market access. We show that these protocols can be designed to guarantee obedience to regulations: providers who do not comply with user-specific safety requirements are statistically driven to self-exclude from the market, while compliant providers remain viable. Our framework provides new theoretical insights into the intersection of statistical testing, mechanism design, and trustworthy AI regulation, offering a foundation for the development of enforceable AI governance mechanisms.

Submission Number: 17