Governing AI Forgetting: Auditing for Machine Unlearning Compliance

2026-02-16

AI Summary

This study addresses the compliance challenges posed by the "right to be forgotten" by proposing the first economic auditing framework for machine unlearning. Integrating certified unlearning theory with regulatory game-theoretic modeling, the work uniquely combines hypothesis testing interpretations with nonlinear equilibrium analysis to tackle the coupled optimization of model utility and detection probability. Through a fixed-point transformation and structured analytical approach, the authors establish the existence and uniqueness of the equilibrium solution and uncover a counterintuitive insight: higher volumes of deletion requests necessitate lower auditing intensity. Furthermore, the analysis reveals that while non-public auditing offers informational advantages, it yields inferior cost-effectiveness. These findings provide rigorous theoretical support for regulatory practices, including those in jurisdictions such as China.

Technology Category

• Machine Learning: Privacy
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Despite legal mandates for the right to be forgotten, AI operators routinely fail to comply with data deletion requests. While machine unlearning (MU) provides a technical solution to remove personal data's influence from trained models, ensuring compliance remains challenging due to the fundamental gap between MU's technical feasibility and regulatory implementation. In this paper, we introduce the first economic framework for auditing MU compliance, by integrating certified unlearning theory with regulatory enforcement. We first characterize MU's inherent verification uncertainty using a hypothesis-testing interpretation of certified unlearning to derive the auditor's detection capability, and then propose a game-theoretic model to capture the strategic interactions between the auditor and the operator. A key technical challenge arises from MU-specific nonlinearities inherent in the model utility and the detection probability, which create complex strategic couplings that traditional auditing frameworks do not address and that also preclude closed-form solutions. We address this by transforming the complex bivariate nonlinear fixed-point problem into a tractable univariate auxiliary problem, enabling us to decouple the system and establish the equilibrium existence, uniqueness, and structural properties without relying on explicit solutions. Counterintuitively, our analysis reveals that the auditor can optimally reduce the inspection intensity as deletion requests increase, since the operator's weakened unlearning makes non-compliance easier to detect. This is consistent with recent auditing reductions in China despite growing deletion requests. Moreover, we prove that although undisclosed auditing offers informational advantages for the auditor, it paradoxically reduces the regulatory cost-effectiveness relative to disclosed auditing.

Problem

• machine unlearning
• right to be forgotten
• compliance auditing
• AI regulation
• data deletion

Innovation

• machine unlearning
• auditing framework
• certified unlearning
• game-theoretic model
• nonlinear fixed-point problem