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.
@article{arxiv.1903.00519,
title = {Aggregating explanation methods for stable and robust explainability},
author = {Laura Rieger and Lars Kai Hansen},
journal= {arXiv preprint arXiv:1903.00519},
year = {2020}
}