Counterfactual explanations show minimal input changes that would flip the model's decision. Format: "If X had been different, prediction would change from A to B." More actionable than feature importance. Example: Loan denial → "If income were $5K higher, loan would be approved." Finding counterfactuals: Optimization to find minimal edit that changes prediction, generative models to produce realistic alternatives, search over discrete changes (for text). Desirable properties: Minimal change (sparse, plausible), proximity to original, achievable/realistic, diverse set of counterfactuals. For text: Token substitutions, insertions, deletions that change classification. Challenge: maintaining fluency and semantic plausibility. Advantages: Actionable insights, intuitively understandable, recourse guidance. Challenges: Multiple valid counterfactuals exist, may suggest unrealistic changes, computationally expensive to find optimal. Applications: Lending/credit decisions, hiring, medical diagnosis, moderation appeal. Tools: DiCE, Alibi, custom search algorithms. Regulatory relevance: GDPR "right to explanation" - counterfactuals provide meaningful explanation of decisions. Powerful for high-stakes decisions.