Explaining a black-box using Deep Variational Information Bottleneck Approach
It addresses the need for concise yet informative explanations in black-box decision systems, which is an incremental improvement in interpretable machine learning.
The paper tackles the problem of redundant explanations in interpretable machine learning by proposing VIBI, a system-agnostic method that selects key features for brief and comprehensive explanations, achieving competitive results on three datasets compared to state-of-the-art methods.
Interpretable machine learning has gained much attention recently. Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system. However, existing interpretable machine learning methods fail to consider briefness and comprehensiveness simultaneously, leading to redundant explanations. We propose the variational information bottleneck for interpretation, VIBI, a system-agnostic interpretable method that provides a brief but comprehensive explanation. VIBI adopts an information theoretic principle, information bottleneck principle, as a criterion for finding such explanations. For each instance, VIBI selects key features that are maximally compressed about an input (briefness), and informative about a decision made by a black-box system on that input (comprehensive). We evaluate VIBI on three datasets and compare with state-of-the-art interpretable machine learning methods in terms of both interpretability and fidelity evaluated by human and quantitative metrics