![[2602.14553] Governing AI Forgetting: Auditing for Machine Unlearning Compliance](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.14553] Governing AI Forgetting: Auditing for Machine Unlearning Compliance
Summary
The paper discusses the challenges of ensuring compliance with data deletion requests in AI systems, proposing a novel economic framework for auditing machine unlearning (MU) compliance.
Why It Matters
As AI systems increasingly handle personal data, the legal right to be forgotten becomes critical. This paper addresses the gap between technical solutions for data deletion and regulatory compliance, offering insights into how auditing can be effectively structured to ensure adherence to these legal mandates.
Key Takeaways
- The paper introduces a new framework for auditing machine unlearning compliance.
- It highlights the verification uncertainties in machine unlearning and proposes a game-theoretic model.
- The findings suggest that increased deletion requests can paradoxically reduce the need for intensive audits.
- Undisclosed auditing may offer advantages but can decrease cost-effectiveness.
- The research addresses complex strategic interactions between auditors and AI operators.
Computer Science > Machine Learning arXiv:2602.14553 (cs) [Submitted on 16 Feb 2026] Title:Governing AI Forgetting: Auditing for Machine Unlearning Compliance Authors:Qinqi Lin, Ningning Ding, Lingjie Duan, Jianwei Huang View a PDF of the paper titled Governing AI Forgetting: Auditing for Machine Unlearning Compliance, by Qinqi Lin and 3 other authors View PDF HTML (experimental) 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 tran...