Google Research· Research

New framework for auditing machine unlearning

Mónica Ribero, Research Scientist, Google Research We introduce a method designed to confidently determine whether there is statistically significant evidence that two sets of data observations come from entirely different underlying distributions. Quick links Paper Share Copy link × Machine unlearning allows AI systems to "forget" specific parts of their training data without the massive cost of retraining a model from scratch. This is essential for regulatory compliance (like GDPR’s "Right to be Forgotten" ), AI safety, and model quality. As models process increasingly massive and highly sensitive datasets, verifying machine unlearning has moved from theoretical ideal to a strict requirement, where developers must now mathematically prove privacy. However, because auditors often don’t have access to the model's internal workings or original training data, they must verify the system strictly by querying it and analyzing the output samples. One method data scientists and researchers rely on for verification is two-sample testing …

Announced

10 Jun 2026 17:34 UTC