arXiv — cs.AI preprintsInternational9 October 2026
CAF\'E: Causal Black-Box Testing of Machine Unlearning
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arXiv:2509.16525v2 Announce Type: replace-cross Abstract: Machine learning models are increasingly deployed as software components that must evolve as requirements change. When specific training records or features must no longer influence a deployed model, machine unlearning aims to remove that influence without retraining from scratch. Because unlearning is often approximate, its effectiveness must be tested. Such tests must often treat the model as a black box, without access to its parameters, training history, or unlearning procedure. Features pose a further challenge: even after a featur
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