Abstract Machine Unlearning (MU), the process of removing specific data influences from trained machine learning models, is critical for regulatory compliance (e.g., GDPR’s right to be forgotten) and for addressing copyright and privacy concerns in large-scale models. While a wide range of methods and metrics have been proposed, systematic evaluations remain fragmented, typically limited in scope by modality, metric coverage, or the number of methods considered. Moreover, the lack of standardized benchmarks leaves several gaps in evaluation protocols, including how to efficiently compare methods, identify optimal hyperparameters, and determine which experimental settings are appropriate for fair and meaningful benchmarking. To address these gaps, we present the most comprehensive MU benchmark to date, evaluating 12 unlearning methods across 8 classification datasets, 4 modalities, several hyperparameters and settings. Based on previous literature and our empirical results, we formalize evaluation protocol desiderata to guide future MU benchmarking. Following these guidelines, we report benchmark results highlighting the best methods within and across domains. To help with method comparison, we also introduce LUMA, a unified metric that aggregates core unlearning dimensions into a single score. Our code is reproducible and extensible to serve as a benchmark for MU research.
D’Angelo et al. (Mon,) studied this question.
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