| 2021 | AMIA | Learning Predictive and Interpretable Timeseries Summaries from ICU Data. | Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross, Finale Doshi-Velez |
| 2021 | CHI | Evaluating the Interpretability of Generative Models by Interactive Reconstruction. | Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez |
| 2021 | ICML | Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. | Andrew Slavin Ross, Finale Doshi-Velez |
| 2020 | AAAI | Ensembles of Locally Independent Prediction Models. | Andrew Slavin Ross, Weiwei Pan, Leo A. Celi, Finale Doshi-Velez |
| 2018 | AAAI | Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. | Andrew Slavin Ross, Finale Doshi-Velez |
| 2018 | AMIA | Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. | Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li-Wei H. Lehman, Andrew Slavin Ross, Aldo Faisal, Finale Doshi-Velez |
| 2017 | IJCAI | Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. | Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez |