Yacoby Y, Green B, C. Griffin J, Doshi-Velez F. "If it didn’t happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment". proceeding at the Human Centered Explainable AI Conference: CHI Workshop on Human Centered Explainable AI (HCXAI. 2022;2 :1-24. Paper

Keramati R, Gottesman O, Celi L, Doshi-Velez F, Brunskill E. Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation, in Proceedings of the Conference on Health, Inference, and Learning. ; 2022 :397-410. Paper

Shein S, Ma Y, Gottesman O, Doshi-Velez F. State Relevance for Off-Policy Evaluation, in proceeding at the International Conference on Machine Learning. Vol 1. ; 2021 :1-20. Publisher's Version Paper

Futoma J, Simons M, Doshi-Velez F, Kamaleswaran R. Power Constrained Bandits, in proceedings at the International Conference on Machine Learning for Healthcare . Vol 4. ; 2021 :1-50. Paper

Pradier M, Zazo J, Parbhoo S, Perlis R, Zazzi M, Doshi-Velez F. Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as Much as Possible. proceeding at the Clinical Research Informatics American Medical Informatics Association Summit (AMIA), . 2021;1 :1-15. Paper

Jacobs M, Pradier M, McCoy T, Perlis R, Doshi-Velez F, Gajos K. How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection. Translational psychiatry. 2021;1 :1-9. Paper

Ross A, Doshi-Velez F. Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. Proceeding at the International Conference on Machine Learning (ICML). 2021;2 :1-23. Paper

Jin L, Schwartz L, Doshi-Velez F, Miller T, Schuler W. Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition. Computational Linguistics. 2021;47 (1) :1-36. Paper

Singh H, Joshi S, Doshi-Velez F, Lakkaraju H. Learning Under Adversarial and Interventional Shifts. Preprint. 2021;1 :1-19. Paper

Kim B, Doshi-Velez F. Machine Learning Techniques for Accountability. AI Magazine. 2021;42 (1) :1. Paper

Jacobs M, He J, Pradier FM, Lam B, Ahn A, McCoy T, Perlis R, Doshi-Velez F, Gajos K. Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. proceedings at the Conference on Human Factors in Computing Systems (CHI). 2021;2 :1-14. Paper

Jacobs M, He J, Pradier FM, Lam B, Ahn A, McCoy T, Perlis R, Doshi-Velez F, Gajos K. Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. proceedings at the Conference on Human Factors in Computing Systems (CHI). 2021;2 :1-14. Paper

Wang K, Shah S, Chen H, Perrault A, Doshi-Velez F, Tambe M. Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning. Preprint. 2021;3 :1-21. Paper

Yao J, Brunskill E, Pan W, Murphy S, Doshi-Velez F. Power Constrained Bandit. proceedings at the Machine Learning for Healthcare Conference. 2021;4 :1-50. Publisher's Version Paper

Futoma J, Simons M, Doshi-Velez F, Kamaleswaran R. Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables. Critical Care Explorations. 2021;1 :1-11. Paper

Narayanan S, Sharma A, Zeng C, Doshi-Velez F. Prediction-focused Mixture Models . proceeding at the International Conference on Machine Learning: Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ICML), 2021. 2021;1 :1-9. Paper

Coker B, Parbhoo S, Doshi-Velez F. Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data. proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML). 2021;1 :1-15. Paper

Coker B, Parbhoo S, Doshi-Velez F. Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data. proceedings at the International Conference on Machine Learning: Workshop on Uncertainty & Robustness in Deep Learning (ICML). 2021;1 :1-15. Paper

Mahinpei A, Clark J, Lage I, Doshi-Velez F, WeiWei P. Promises and Pitfalls of Black-Box Concept Learning Models. proceeding at the International Conference on Machine Learning: Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI,. 2021;1 :1-13. Paper

Wu M, Parbhoo S, Hughes M, Roth V, Doshi-Velez F. Optimizing for Interpretability in Deep Neural Networks with Tree Regularization. Journal of AI Research (JAIR). 2021;1 :1-37. Paper