Unlearning removes specific knowledge or capabilities from trained models for safety, privacy, or compliance. Motivations: Remove copyrighted content, forget personal data (GDPR right to erasure), eliminate harmful capabilities, remove sensitive information. Approaches: Fine-tuning to forget: Train on "forget" examples with reversed labels or random outputs. Gradient ascent: Increase loss on data to unlearn (opposite of learning). Representation surgery: Edit embeddings to remove specific concepts. Influence functions: Approximate effect of removing specific training examples. Challenges: Verification: How to confirm knowledge is truly removed, not just suppressed? Generalization: Unlearn from paraphrased queries too. Capability preservation: Don't damage related useful capabilities. Relearning risk: Knowledge may resurface with prompting. Distinction from editing: Editing changes facts, unlearning removes them entirely. Applications: Copyright compliance, privacy (remove PII), safety (remove harmful knowledge). Current state: Active research, no foolproof methods, red-teaming needed to verify. Tools: Various research implementations, tofu benchmark. Important for responsible AI deployment.