Related papers: Evaluation of Similarity-based Explanations

Evaluation of Similarity-based Explanations

URL: http://arxiv.org/abs/2006.04528v2
Date: Mon, 22 Mar 2021 22:15:53 GMT
Title: Evaluation of Similarity-based Explanations
Authors: Kazuaki Hanawa, Sho Yokoi, Satoshi Hara, Kentaro Inui
Abstract summary: We investigated relevance metrics that can provide reasonable explanations to users. Our experiments revealed that the cosine similarity of the gradients of the loss performs best. Some metrics perform poorly in our tests and analyzed the reasons of their failure.
Score: 36.10585276728203
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Explaining the predictions made by complex machine learning models helps users to understand and accept the predicted outputs with confidence. One promising way is to use similarity-based explanation that provides similar instances as evidence to support model predictions. Several relevance metrics are used for this purpose. In this study, we investigated relevance metrics that can provide reasonable explanations to users. Specifically, we adopted three tests to evaluate whether the relevance metrics satisfy the minimal requirements for similarity-based explanation. Our experiments revealed that the cosine similarity of the gradients of the loss performs best, which would be a recommended choice in practice. In addition, we showed that some metrics perform poorly in our tests and analyzed the reasons of their failure. We expect our insights to help practitioners in selecting appropriate relevance metrics and also aid further researches for designing better relevance metrics for explanations.

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