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Despite a growing literature on explaining neural networks, no consensus hasbeen reached on how to explain a neural network decision or how to evaluate anexplanation. Our contributions in this paper are twofold. First, we investigateschemes to combine explanation methods and reduce model uncertainty to obtain asingle aggregated explanation. We provide evidence that the aggregation isbetter at identifying important features, than on individual methods.Adversarial attacks on explanations is a recent active research topic. As oursecond contribution, we present evidence that aggregate explanations are muchmore robust to attacks than individual explanation methods.

Aggregating explanation methods for stable and robust explainability