explainability

Both explain one prediction by crediting features, but only one is backed by a uniqueness theorem. What interviewers reward is knowing SHAP's game-theory guarantees, LIME's instability, and when each fits. Here is the answer.

Regulators and enterprises increasingly require explanations for AI decisions, and the two terms get thrown around loosely. What lands is separating inherently interpretable models from post-hoc explanations and picking the right technique for the stakes.

For consequential decisions, 'the model said so' will not satisfy a regulator. Adverse-action notices and the right to explanation limit which model you can even ship. Here is the governance view.