Interpreting Predictive Models for Human-in-the-Loop Analytics
Wednesday, December 5, 2018 - 10:30am - 11:30am
Machine learning is increasingly used to inform consequential decisions. Yet, these predictive models have been found to exhibit unexpected defects when trained on real-world observational data, which are plagued with confounders and biases. Thus, it is critical to involve domain experts in an interactive process of developing predictive models; interpretability offers a promising way to facilitate this interaction. We propose a novel approach to interpreting complex, blackbox machine learning models by constructing simple decision trees that summarize their reasoning process. Our algorithm leverages active learning to extract richer and more accurate interpretations than several baselines. Furthermore, we prove that by generating a sufficient amount of data through our active learning strategy, the extracted decision tree converges to the exact decision tree, implying that we provably avoid overfitting. We evaluate our algorithm on a random forest to predict diabetes risk on a real electronic medical record dataset, and show that it produces significantly more accurate interpretations than several baselines. We also conduct a user study demonstrating that humans are able to better reason about our interpretations than state-of-the-art rule lists. We then perform a case study with domain experts (physicians) regarding our diabetes risk prediction model, and describe several insights they derived using our interpretation. Of particular note, the physicians discovered an unexpected causal issue by investigating a subtree in our interpretation; we were able to then verify that this endogeneity indeed existed in our data, underscoring the value of interpretability.