What is the difference between explainability and interpretability, and how do you explain a model's decision?
Regulators and enterprises increasingly demand explanations for AI decisions, and the two terms get used loosely. The signal is separating inherently interpretable models from post-hoc explanations and naming the right technique for the stakes.
Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.
Regulators and enterprises increasingly demand explanations for AI decisions, and the two terms get used loosely. The signal is separating inherently interpretable models from post-hoc explanations and naming the right technique for the stakes.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.