Machine unlearning: Difference between revisions
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Latest revision as of 00:24, 20 January 2026
Machine unlearning
A technique that involves selectively removing the influences of specific training data points from a trained machine learning model, such as to remove unwanted capabilities or knowledge in a foundation model, or to enable a user to request the removal of their records from a model. Efficient approximate unlearning techniques may not require retraining the ML model from scratch.
Source: NIST AI 100-2e2025 | Category: