AGENTIC-SYSTEMSMay 28, 20261 min read12 views

RULER: Representation-Level Verification of Machine Unlearning

Current machine unlearning methods often only verify results at the output level, leaving forgotten data potentially encoded within intermediate representations. To address this loophole, researchers have introduced RULER, a new set of metrics that verifies unlearning at the representation level, ensuring higher accuracy and security for AI systems.

Computer Science > Artificial Intelligence

Title:RULER: Representation-Level Verification of Machine Unlearning

View PDF HTML (experimental)Abstract:Machine unlearning aims to remove the influence of specific training records from a deployed model without retraining from scratch. Current protocols verify this at the output level through membership inference, retain accuracy, and forget-set accuracy, but a model can satisfy all three whilst still encoding forgotten records in its intermediate representations. We introduce RULER, a set of representation-level verification metrics. The oracle-comparative metric M2 measures whether forget-set records occupy the same representational position as in a model retrained without them. The oracle-free metric M4 detects residuals from the unlearned model's internal similarity structure alone, without retraining. Four approximate unlearning methods all pass output-level evaluation, yet under a linear mixed-effects model M2 detects significant residuals in 10 of 12 conditions (p<0.05), with effect sizes growing as the forget fraction increases. A fifth method, Bad Teacher, shows the same residuals despite a different forgetting mechanism. M4 acts as a pre-unlearning diagnostic across tabular, image, clinical text, and face-identity settings: it detects identity-level memorisation in face recognition models where no tested method fully erases the signal.

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Source: arXiv cs.AI Recent

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