When a user invokes their "right to be forgotten" under GDPR, a company must prove their AI model has actually erased that person's data influence. But how do you verify that a model has truly forgotten something? That question has quietly become one of the hardest problems in responsible AI, and the tools we've been using to answer it are fundamentally broken. Google Research just published a framework that fixes the core flaw.

The new approach, called Regularized f-Divergence Kernel Tests, was presented at AISTATS 2026 and is designed to make auditing ML models much more sensitive, flexible, and accurate. The code is publicly available on GitHub.

The broken audit

The standard approach to verifying machine unlearning relies on two-sample testing -- a statistical method that checks whether outputs from two models come from different underlying distributions. The logic seems sound: compare a model that supposedly forgot a record against one retrained from scratch without it. If their outputs look statistically different, unlearning failed.

The problem is that this test is too sensitive in the wrong direction. Exact unlearning mechanisms that retrain from scratch without access to the forget data are safe by definition. However, two-sample tests incorrectly flag them as unsafe due to distributional differences with the reference model. The three-sample test overcomes this issue.

There's also a theoretical ceiling. Recent work shows that an AI model can never perfectly "forget" data just by tweaking its current settings; unless it re-traces every step of its original training, it will always leave behind a permanent footprint of the information it was supposed to delete. This means a sensitive enough two-sample test will always flag approximate unlearning as a failure, even when it's working correctly.