Google Research·· 2026-06-11
New framework for auditing machine unlearning
New framework for auditing machine unlearning
AI summary
At AISTATS 2026, Google Research proposed Regularized f-Divergence Kernel Tests for machine unlearning audits, using relative distances to determine whether models are closer to safely retrained versions or original compromised models.
Selection record
Threshold 60Official, first-handFirst 38Second 38
Not admittedSum of both 76 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：机器遗忘审计框架，属AI安全与评测技术
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: Google Research · research.google