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Feature based explanations, that provide importance of each feature towardsthe model prediction, is arguably one of the most intuitive ways to explain amodel. In this paper, we establish a novel set of evaluation criteria for suchfeature based explanations by robustness analysis. In contrast to existingevaluations which require us to specify some way to "remove" features thatcould inevitably introduces biases and artifacts, we make use of the subtlernotion of smaller adversarial perturbations. By optimizing towards our proposedevaluation criteria, we obtain new explanations that are loosely necessary andsufficient for a prediction. We further extend the explanation to extract theset of features that would move the current prediction to a target class byadopting targeted adversarial attack for the robustness analysis. Throughexperiments across multiple domains and a user study, we validate theusefulness of our evaluation criteria and our derived explanations.

Evaluations and Methods for Explanation through Robustness Analysis