Interpreting Blackbox Models via Model Extraction
  Interpretability has become incredibly important as machine learning is
increasingly used to inform consequential decisions. We propose to construct
global explanations of complex, blackbox models in the form of a decision tree
approximating the original model---as long as the decision tree is a good
approximation, then it mirrors the computation performed by the blackbox model.
We devise a novel algorithm for extracting decision tree explanations that
actively samples new training points to avoid overfitting. We evaluate our
algorithm on a random forest to predict diabetes risk and a learned controller
for cart-pole. Compared to several baselines, our decision trees are both
substantially more accurate and equally or more interpretable based on a user
study. Finally, we describe several insights provided by our interpretations,
including a causal issue validated by a physician.
