Targeted Forgetting: Why AI Can’t Just ‘Unlearn’ — And What TRU Fixes
Delete is a comforting word. A user deletes an account. A marketplace removes a product. A shopper corrects a preference history because the recommendation engine has decided, with touching confidence, that one accidental click reveals a permanent love of baby strollers, golf gloves, or suspiciously ugly jackets. In a normal database, deletion sounds like a row-level operation. Remove the row, update the index, move on with life. In a trained recommender model, deletion is less tidy. The deleted data may already have shaped user embeddings, item popularity, image-text fusion layers, and ranking behavior. The row is gone, but its ghost may still be politely recommending itself. ...