Google's new Regularized f-Divergence Kernel Tests boost machine-unlearning audits
Google Research unveils Regularized f‑Divergence Kernel Tests, a statistical framework that detects unlearning failures with higher sensitivity and lower sample cost than traditional two‑sample tests. This lets auditors prove GDPR‑style forgetting without full model access, cutting computational expense and tightening privacy guarantees for large‑scale AI systems.