Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols

• URL: http://arxiv.org/abs/2503.06991v1
• Date: Mon, 10 Mar 2025 07:11:34 GMT
• Title: Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols
• Authors: Yongwoo Kim, Sungmin Cha, Donghyun Kim,
• Abstract summary: We conduct a new comprehensive evaluation that employs representation-based evaluations of unlearned model under large-scale scenarios.<n>Our analysis reveals that current state-of-the-art unlearning approaches either completely degrade the representational quality of the unlearned model.<n>We introduce a novel unlearning evaluation setup from a transfer learning perspective, in which the forget set classes exhibit semantic similarity to downstream task classes.
• Score: 14.961054239793356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine unlearning is a process to remove specific data points from a trained model while maintaining the performance on retain data, addressing privacy or legal requirements. Despite its importance, existing unlearning evaluations tend to focus on logit-based metrics (i.e., accuracy) under small-scale scenarios. We observe that this could lead to a false sense of security in unlearning approaches under real-world scenarios. In this paper, we conduct a new comprehensive evaluation that employs representation-based evaluations of the unlearned model under large-scale scenarios to verify whether the unlearning approaches genuinely eliminate the targeted forget data from the model's representation perspective. Our analysis reveals that current state-of-the-art unlearning approaches either completely degrade the representational quality of the unlearned model or merely modify the classifier (i.e., the last layer), thereby achieving superior logit-based evaluation metrics while maintaining significant representational similarity to the original model. Furthermore, we introduce a novel unlearning evaluation setup from a transfer learning perspective, in which the forget set classes exhibit semantic similarity to downstream task classes, necessitating that feature representations diverge significantly from those of the original model. Our comprehensive benchmark not only addresses a critical gap between theoretical machine unlearning and practical scenarios, but also establishes a foundation to inspire future research directions in developing genuinely effective unlearning methodologies.

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