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Recent studies evaluating various criteria for explainable artificialintelligence (XAI) suggest that fidelity, stability, and comprehensibility areamong the most important metrics considered by users of AI across a diversecollection of usage contexts. We consider these criteria as applied tofeature-based attribution methods, which are amongst the most prevalent in XAIliterature. Going beyond standard correlation, methods have been proposed thathighlight what should be minimally sufficient to justify the classification ofan input (viz. pertinent positives). While minimal sufficiency is an attractiveproperty akin to comprehensibility, the resulting explanations are often toosparse for a human to understand and evaluate the local behavior of the model.To overcome these limitations, we incorporate the criteria of stability andfidelity and propose a novel method called Path-Sufficient Explanations Method(PSEM) that outputs a sequence of stable and sufficient explanations for agiven input of strictly decreasing size (or value) -- from original input to aminimally sufficient explanation -- which can be thought to trace the localboundary of the model in a stable manner, thus providing better intuition aboutthe local model behavior for the specific input. We validate these claims, bothqualitatively and quantitatively, with experiments that show the benefit ofPSEM across three modalities (image, tabular and text) as well as versus otherpath explanations. A user study depicts the strength of the method incommunicating the local behavior, where (many) users are able to correctlydetermine the prediction made by a model.

When Stability meets Sufficiency: Informative Explanations that do not Overwhelm