  In the rapidly growing literature on explanation algorithms, it often remains
unclear what precisely these algorithms are for and how they should be used. We
argue that this is because explanation algorithms are often mathematically
complex but don't admit a clear interpretation. Unfortunately, complex
statistical methods that don't have a clear interpretation are bound to lead to
errors in interpretation, a fact that has become increasingly apparent in the
literature. In order to move forward, papers on explanation algorithms should
make clear how precisely the output of the algorithms should be interpreted.
They should also clarify what questions about the function can and cannot be
answered given the explanations. Our argument is based on the distinction
between statistics and their interpretation. It also relies on parallels
between explainable machine learning and applied statistics.
