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arXiv · 1903.00519

Aggregating explanation methods for stable and robust explainability

Laura Rieger·Lars Kai Hansen
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Abstract

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.

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BibTeXRIS

Laura Rieger, Lars Kai Hansen. 2019-03-01. Aggregating explanation methods for stable and robust explainability. https://arxiv.org/abs/1903.00519

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