Examples are not enough, learn to criticize! Criticism for Interpretability

Example-based explanations are widely used in the effort to improve the interpretability of highly complex distributions. However, prototypes alone are rarely sufficient to represent the gist of the complexity. In order for users to construct better mental models and understand complex data distributions, we also need {em criticism} to explain what are textit{not} captured by prototypes. Motivated by the Bayesian model criticism framework, we develop texttt{MMD-critic} which efficiently learns prototypes and criticism, designed to aid human interpretability. A human subject pilot study shows that the texttt{MMD-critic} selects prototypes and criticism that are useful to facilitate human understanding and reasoning. We also evaluate the prototypes selected by texttt{MMD-critic} via a nearest prototype classifier, showing competitive performance compared to baselines.