治理AI遗忘：审计机器无学习合规性
Governing AI Forgetting: Auditing for Machine Unlearning Compliance
AI总结：
本文提出首个经济框架用于审计机器无学习的合规性，通过博弈论模型分析审计员与运营商的战略互动，并揭示审计强度随删除请求增加而减少的反直觉结论。
摘要：
尽管法律要求享有被遗忘的权利，AI运营商经常未能遵守数据删除请求。虽然机器无学习（MU）提供了去除训练模型中个人数据影响的技术解决方案，但确保合规性仍然具有挑战性，因为MU的技术可行性与监管实施之间存在根本性差距。在本文中，我们引入了首个经济框架用于审计MU合规性，通过将认证无学习理论与监管执法相结合。我们首先使用认证无学习的假设检验解释来表征MU的固有验证不确定性，以推导审计员的检测能力，然后提出一个博弈论模型来捕捉审计员和运营商之间的战略互动。一个关键的技术挑战来自于模型效用和检测概率中固有的MU特定非线性特性，这会产生复杂的战略耦合，传统审计框架无法解决，也无法得到闭式解。我们通过将复杂的双变量非线性固定点问题转换为可处理的单变量辅助问题来解决这一问题，从而能够解耦系统并建立平衡的存在性、唯一性和结构性属性，而无需依赖显式解。反直觉地，我们的分析揭示了审计员可以最优地减少检查强度，因为运营商的削弱无学习使不合规更容易被检测到。这与近年来中国审计减少一致，尽管删除请求增加。此外，我们证明虽然未披露审计为审计员提供了信息优势，但反直觉地，这反而降低了监管的成本效益相对披露审计。
英文摘要：
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.
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