Explainability, meanwhile, is the extent to which the internal mechanics of a machine or deep learning system can be explained in human terms.

It’s easy to miss the subtle difference with interpretability, but consider it like this: interpretability is about being able to discern the mechanics without necessarily knowing why. Explainability is being able to quite literally explain what is happening.

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