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Feature based local attribution methods are amongst the most prevalent inexplainable artificial intelligence (XAI) literature. Going beyond standardcorrelation, recently, methods have been proposed that highlight what should beminimally sufficient to justify the classification of an input (viz. pertinentpositives). While minimal sufficiency is an attractive property, the resultingexplanations are often too sparse for a human to understand and evaluate thelocal behavior of the model, thus making it difficult to judge its overallquality. To overcome these limitations, we propose a novel method calledPath-Sufficient Explanations Method (PSEM) that outputs a sequence ofsufficient explanations for a given input of strictly decreasing size (orvalue) -- from original input to a minimally sufficient explanation -- whichcan be thought to trace the local boundary of the model in a smooth manner,thus providing better intuition about the local model behavior for the specificinput. We validate these claims, both qualitatively and quantitatively, withexperiments that show the benefit of PSEM across all three modalities (image,tabular and text). 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.

Towards Better Model Understanding with Path-Sufficient Explanations