  Machine learning models that offer excellent predictive performance often
lack the interpretability necessary to support integrated human machine
decision-making. In clinical medicine and other high-risk settings, domain
experts may be unwilling to trust model predictions without explanations. Work
in explainable AI must balance competing objectives along two different axes:
1) Explanations must balance faithfulness to the model's decision-making with
their plausibility to a domain expert. 2) Domain experts desire local
explanations of individual predictions and global explanations of behavior in
aggregate. We propose to train a proxy model that mimics the behavior of the
trained model and provides fine-grained control over these trade-offs. We
evaluate our approach on the task of assigning ICD codes to clinical notes to
demonstrate that explanations from the proxy model are faithful and replicate
the trained model behavior.
