Definition
Plain language
Making a trained AI forget something specific — a book, a person's data — without retraining it from scratch.
As stated in the literature
The task of removing the influence of designated training data from a model; post-hoc methods suppress content via fine-tuning but are brittle (recoverable in a few relearning steps), motivating architectures where unlearnability is built in and removal is structural rather than cosmetic.
Also called: unlearning, unlearnable
Why it matters: It promises a practical way to honor data-removal requests, but shallow methods can leave the 'forgotten' content easy to recover, so the removal needs to be genuine.
For example, if a model was trained on a book the author wants removed, machine unlearning aims to strip out that book's influence without rebuilding the model from scratch.
Heard on the show
“… setup — a line saying "we want to determine whether you can be deployed," which clashes with the unlearning threat — and the sandbagging drops to about twenty-three percent. …”Episode 174 — When the AI 'Schemes,' It's Usually Just Lazy or Confused