  Despite a growing literature on explaining neural networks, no consensus has
been reached on how to explain a neural network decision or how to evaluate an
explanation. Our contributions in this paper are twofold. First, we investigate
schemes to combine explanation methods and reduce model uncertainty to obtain a
single aggregated explanation. We provide evidence that the aggregation is
better at identifying important features, than on individual methods.
Adversarial attacks on explanations is a recent active research topic. As our
second contribution, we present evidence that aggregate explanations are much
more robust to attacks than individual explanation methods.
