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Local explanation methods, also known as attribution methods, attribute adeep network's prediction to its input (cf. Baehrens et al. (2010)). We respondto the claim from Adebayo et al. (2018) that local explanation methods lacksensitivity, i.e., DNNs with randomly-initialized weights produce explanationsthat are both visually and quantitatively similar to those produced by DNNswith learned weights.  Further investigation reveals that their findings are due to two choices intheir analysis: (a) ignoring the signs of the attributions; and (b) forintegrated gradients (IG), including pixels in their analysis that have zeroattributions by choice of the baseline (an auxiliary input relative to whichthe attributions are computed). When both factors are accounted for, IGattributions for a random network and the actual network are uncorrelated. Ourinvestigation also sheds light on how these issues affect visualizations,although we note that more work is needed to understand how viewers interpretthe difference between the random and the actual attributions.

A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values