This article investigates Machine Unlearning (MU), which is focused on neural models inadvertently retaining personal or sensitive data. A novel approach is introduced to achieve precise and selective forgetting within…
Continue readingThe article presents F AIRDEBUGGER, a system designed to identify and explain instances of fairness violations in the outcomes of random forest classifiers. It uses machine unlearning to estimate changes…
Continue readingThe article discusses the importance of unlearning in machine learning models, particularly in the context of data regulations like GDPR. Unlearning involves removing the influence of certain data from a…
Continue readingThis study delves into the concept of ‘the right to be forgotten’ in line with data protection regulations like the GDPR. It focuses on Federated Online Learning to Rank (FOLTR)…
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