Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

Authors

  • Mike Wu Stanford University
  • Michael Hughes Harvard University
  • Sonali Parbhoo University of Basel
  • Maurizio Zazzi University of Siena
  • Volker Roth University of Basel
  • Finale Doshi-Velez Harvard University

Keywords:

explainable AI, neural networks, decision trees

Abstract

The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using intuitive toy examples as well as medical tasks for treating sepsis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than simpler L1 or L2 penalties without sacrificing predictive power.

Downloads

Published

2018-04-25

How to Cite

Wu, M., Hughes, M., Parbhoo, S., Zazzi, M., Roth, V., & Doshi-Velez, F. (2018). Beyond Sparsity: Tree Regularization of Deep Models for Interpretability. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11501