Explaining a black-box using Deep Variational Information Bottleneck Approach

02/19/2019
by   Seojin Bang, et al.
8

Briefness and comprehensiveness are necessary in order to give a lot of information concisely in explaining a black-box decision system. However, existing interpretable machine learning methods fail to consider briefness and comprehensiveness simultaneously, which may lead to redundant explanations. We propose a system-agnostic interpretable method that provides a brief but comprehensive explanation by adopting the inspiring information theoretic principle, information bottleneck principle. Using an information theoretic objective, VIBI selects instance-wise key features that are maximally compressed about an input (briefness), and informative about a decision made by a black-box on that input (comprehensive). The selected key features act as an information bottleneck that serves as a concise explanation for each black-box decision. We show that VIBI outperforms other interpretable machine learning methods in terms of both interpretability and fidelity evaluated by human and quantitative metrics.

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