When a black-box classifier processes an input to render a prediction, which input features are relevant and why? We propose to answer this question by efficiently marginalizing over the universe of plausible alternative values for a subset of features by conditioning a generative model of the input distribution on the remaining features. In contrast with recent approaches that compute alternative feature values ad-hoc-generating counterfactual inputs far from the natural data distributionour model-agnostic method produces realistic explanations, generating plausible inputs that either preserve or alter the classification confidence. When applied to image classification, our method produces more compact and relevant per-feature saliency assignment, with fewer artifacts compared to previous methods.
To more explicitly leverage the feature distribution, one can marginalize over the universe of plausible alternative values for the select features by conditioning a generative model of the input distribution given the remaining features REF .
Explaining Image Classifiers by Counterfactual Generation
arXiv: Computer Vision and Pattern Recognition
Computer Science