Causal Fuzzing for Verifying Machine Unlearning

• URL: http://arxiv.org/abs/2509.16525v1
• Date: Sat, 20 Sep 2025 04:19:37 GMT
• Title: Causal Fuzzing for Verifying Machine Unlearning
• Authors: Anna Mazhar, Sainyam Galhotra,
• Abstract summary: CAF'E is a new framework that unifies datapoint- and feature-level unlearning for verification of black-box ML models.<n>Our evaluation shows that CAF'E successfully detects residual influence missed by baselines while maintaining computational efficiency.
• Score: 9.923981046985771
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As machine learning models become increasingly embedded in decision-making systems, the ability to "unlearn" targeted data or features is crucial for enhancing model adaptability, fairness, and privacy in models which involves expensive training. To effectively guide machine unlearning, a thorough testing is essential. Existing methods for verification of machine unlearning provide limited insights, often failing in scenarios where the influence is indirect. In this work, we propose CAF\'E, a new causality based framework that unifies datapoint- and feature-level unlearning for verification of black-box ML models. CAF\'E evaluates both direct and indirect effects of unlearning targets through causal dependencies, providing actionable insights with fine-grained analysis. Our evaluation across five datasets and three model architectures demonstrates that CAF\'E successfully detects residual influence missed by baselines while maintaining computational efficiency.

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