Unlearning: Can a Model Forget on Request?

Machine unlearning is the attempt to remove the influence of specific training data from a trained model without retraining it. The methods are plausible; the problem is that nobody has a reliable way to tell whether one worked, and a model that no longer emits a fact under one probe may still contain it. What is being asked for Three distinct demands travel under one word, and they have different success conditions. Legal deletion. A data subject exercises a right to erasure and their data must be removed from processing. Whether that extends to model weights is contested and unsettled; regulators have not converged, and the practical question of what erasure means once the data has been trained on is live. Capability removal. Remove a hazardous ability rather than a specific document. This is a safety goal and is arguably harder, because the capability is distributed across a large amount of related training data rather than concentrated in one place. Correction.