No Matter How You Slice It: Machine Unlearning with SISA Comes at the Expense of Minority Classes

Abstract

Machine unlearning using the SISA technique promises a significant speedup in model retraining with only minor sacrifices in performance. Even greater speedups can be achieved in a distribution-aware setting, where training samples are sorted by their individual unlearning likelihood. Yet, the side effects of these techniques on model performance are still poorly understood. In this paper, we layout the impact of SISA unlearning in settings where classes are imbalanced, as well as in settings where class membership is correlated with unlearning likelihood. We show that the performance decrease that is associated with using SISA is primarily carried by minority classes and that conventional techniques for imbalanced datasets are unable to close this gap. We demonstrate that even for a class imbalance of just 1:10, simply down-sampling the dataset to a more balanced single shard outperforms SISA while providing the same unlearning speedup. We show that when minority class membership is correlated with a higher- or lower- than -average unlearning likelihood, the accuracy of those classes can be either improved or diminished in distribution-aware SISA models. This relationship makes the model sensitive to naturally occurring unlearning likelihood correlations. While SISA models tend to be sensitive to class distribution we found no impact on imbalanced subgroups or model fairness. Our work contributes to a better understanding of the side effects and trade-offs that are associated with SISA training.