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

The authors explore the compelling question of how to develop interpretable machine learning methods using prototypes and criticisms. The paper was well written and clear, even for a non-expert in the field like myself. The mathematical results appear to be sound. It is hard for me to assess the originality of the work in the field of machine learning, but I imagine that there is work on training with both positive and negative examples. At the very least, within the human category learning literature the issue of learning a concept through examples of the concept and non-examples has been explored.