Causal Fuzzing for Verifying Machine Unlearning
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908549165088768 |
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| author | Mazhar, Anna Galhotra, Sainyam |
| author_facet | Mazhar, Anna Galhotra, Sainyam |
| contents | 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 CAFÉ, a new causality based framework that unifies datapoint- and feature-level unlearning for verification of black-box ML models. CAFÉ 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 CAFÉ successfully detects residual influence missed by baselines while maintaining computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Fuzzing for Verifying Machine Unlearning Mazhar, Anna Galhotra, Sainyam Software Engineering Artificial Intelligence Machine Learning 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 CAFÉ, a new causality based framework that unifies datapoint- and feature-level unlearning for verification of black-box ML models. CAFÉ 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 CAFÉ successfully detects residual influence missed by baselines while maintaining computational efficiency. |
| title | Causal Fuzzing for Verifying Machine Unlearning |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.16525 |