Robustness in machine learning explanations: does it matter?

Abstract

The explainable AI literature contains multiple notions of what an explanation is and what desiderata explanations should satisfy. One implicit source of disagreement is how far the explanations should reflect real patterns in the data or the world. This disagreement underlies debates about other desiderata, such as how robust explanations are to slight perturbations in the input data. I argue that robustness is desirable to the extent that we're concerned about finding real patterns in the world. The import of real patterns differs according to the problem context. In some contexts, non-robust explanations can constitute a moral hazard. By being clear about the extent to which we care about capturing real patterns, we can also determine whether the Rashomon Effect is a boon or a bane.