Machine unlearning is an emerging paradigm to remove the influence of specific training data (i.e., the forget set) from a model while preserving its knowledge of the rest of the data (i.e., the retain set). Previous approaches assume the forget data to be uniformly distributed from all training datapoints. However, if the data to unlearn is dominant in one group, we empirically show that performance for this group degrades, leading to fairness issues. This work tackles the overlooked problem of non-uniformly distributed forget sets, which we call group-robust machine unlearning, by presenting a simple, effective strategy that mitigates the performance loss in dominant groups via sample distribution reweighting. Moreover, we present MIU (Mutual Information-aware Machine Unlearning), the first approach for group robustness in approximate machine unlearning. MIU minimizes the mutual information between model features and group information, achieving unlearning while reducing performance degradation in the dominant group of the forget set. Additionally, MIU exploits sample distribution reweighting and mutual information calibration with the original model to preserve group robustness. We conduct experiments on three datasets and show that MIU outperforms standard methods, achieving unlearning without compromising model robustness. Source code available at https://github.com/tdemin16/group-robust_machine_unlearning.

本研究解决了机器遗忘领域中未考虑的忘记数据在群体中的不均匀分布问题，提出了一种新的群体稳健机器遗忘策略。通过样本分布重加权和互信息最小化的方法，该研究显著改善了在主导群体中的性能，确保模型在遗忘特定数据的同时不会影响其对其他数据的知识保持。实验结果表明，这种新方法在三个数据集上优于传统方法，展示了其在避免性能下降的同时实现机器遗忘的潜力。

群体稳健的机器遗忘