Learning to Scaffold: Optimizing Model Explanations for Teaching. (arXiv:2204.10810v1 [cs.LG])

Modern machine learning models are opaque, and as a result there is a
burgeoning academic subfield on methods that explain these models' behavior.
However, what is the precise goal of providing such explanations, and how can
we demonstrate that explanations achieve this goal? Some research argues that
explanations should help teach a student (either human or machine) to simulate
the model being explained, and that the quality of explanations can be measured
by the simulation accuracy of students on unexplained …

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