We provide fundamental principles for interpretable ML, and dispel common misunderstanding that dilute the importance of this crucial topic.
There is now vast and confusing literature on some combination of interpretability and explainability. Much literature on explainability confounds it with interpretability/ comprehensibility thus obscuring the arguments, detracting from their precision, and failing to convey the relative importance and use-cases of the two topics in practice. Some of the literature discussed topics in such generality that its lessons have little bearing on any specific problem.
Most of it assumes that one would explain a black box without consideration of whether there is an interpretable model of the same accuracy.