CAF\'E: Causal Black-Box Testing of Machine Unlearning

תקציר מקורי באנגליתarXiv: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 feature is removed from a model's inputs, its influence can persist through downstream features. Many existing checks examine only the feature's direct use and can therefore certify a model that still depen