Auditing of Unlearning Algorithms

Sahasrajit Sarmasarkar; Anastasia Koloskova; Sanmi Koyejo

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arXiv · 2607.05898

Auditing of Unlearning Algorithms

Sahasrajit Sarmasarkar·Anastasia Koloskova·Sanmi Koyejo

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Abstract

Evaluating whether unlearning algorithms truly remove training data influence remains an open challenge. We propose a practical auditor that computes data-dependent lower bounds on the unlearning parameter $\varepsilon$ using membership inference attacks. Evaluating multiple unlearning algorithms, we find a sharp separation: algorithms with rigorous guarantees, such as model clipping and rewind-to-delete, achieve very small $\varepsilon$ bounds that do not falsify their unlearning guarantees, whereas empirical methods such as Hessian-based unlearning, interleaved ascent-descent, ascent on the forget set, and fine-tuning on the retain set exhibit large bounds, indicating poor unlearning. Our auditor provides a practical tool for empirically falsifying unlearning claims through a hypothesis-testing framework, and we validate it on CIFAR-100 and Shakespeare text.

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Sahasrajit Sarmasarkar, Anastasia Koloskova, Sanmi Koyejo. 2026-07-07. Auditing of Unlearning Algorithms. https://arxiv.org/abs/2607.05898

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