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Machine learning models that offer excellent predictive performance oftenlack the interpretability necessary to support integrated human machinedecision-making. In clinical medicine and other high-risk settings, domainexperts may be unwilling to trust model predictions without explanations. Workin explainable AI must balance competing objectives along two different axes:1) Explanations must balance faithfulness to the model's decision-making withtheir plausibility to a domain expert. 2) Domain experts desire localexplanations of individual predictions and global explanations of behavior inaggregate. We propose to train a proxy model that mimics the behavior of thetrained model and provides fine-grained control over these trade-offs. Weevaluate our approach on the task of assigning ICD codes to clinical notes todemonstrate that explanations from the proxy model are faithful and replicatethe trained model behavior.

Faithful and Plausible Explanations of Medical Code Predictions