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. In fact, most works rely on manually assessing the explanation to
evaluate the quality of a method. This injects uncertainty in the explanation
process along several dimensions: Which explanation method to apply? Who should
we ask to evaluate it and which criteria should be used for the evaluation? 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. Our findings show that the aggregation is more robust,
well-aligned with human explanations and can attribute relevance to a broader
set of features (completeness). Second, we propose a novel way of evaluating
explanation methods that circumvents the need for manual evaluation and is not
reliant on the alignment of neural networks and humans decision processes.

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