Human-in-the-Loop Interpretability Prior. - Isaac Lage; Andrew Slavin Ross; Samuel J. Gershman; Been Kim; Finale Doshi-Velez (NeurIPS, 2018)

Evaluating Reinforcement Learning Algorithms in Observational Health Settings. - Omer Gottesman; Fredrik D. Johansson; Joshua Meier; Jack Dent; Donghun Lee; Srivatsan Srinivasan; Linying Zhang; Yi Ding; David Wihl; Xuefeng Peng; Jiayu Yao; Isaac Lage; et al. (CoRR, 2018)

Toward Robust Policy Summarization. - Isaac Lage; Daphna Lifschitz; Finale Doshi-Velez; Ofra Amir (AAMAS, 2019)

Human Evaluation of Models Built for Interpretability. - Isaac Lage; Emily Chen; Jeffrey He; Menaka Narayanan; Been Kim; Samuel J. Gershman; Finale Doshi-Velez (HCOMP, 2019)

Exploring Computational User Models for Agent Policy Summarization. - Isaac Lage; Daphna Lifschitz; Finale Doshi-Velez; Ofra Amir (IJCAI, 2019)

An Evaluation of the Human-Interpretability of Explanation. - Isaac Lage; Emily Chen; Jeffrey He; Menaka Narayanan; Been Kim; Sam Gershman; Finale Doshi-Velez (CoRR, 2019)

Learning Interpretable Concept-Based Models with Human Feedback. - Isaac Lage; Finale Doshi-Velez (CoRR, 2020)

Promises and Pitfalls of Black-Box Concept Learning Models. - Anita Mahinpei; Justin Clark; Isaac Lage; Finale Doshi-Velez; Weiwei Pan (CoRR, 2021)

(When) Are Contrastive Explanations of Reinforcement Learning Helpful? - Sanjana Narayanan; Isaac Lage; Finale Doshi-Velez (CoRR, 2022)

When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making. - Sean McGrath; Parth Mehta 003; Alexandra Zytek; Isaac Lage; Himabindu Lakkaraju (Trans. Mach. Learn. Res., 2023)

Towards Integrating Personal Knowledge into Test-Time Predictions. - Isaac Lage; Sonali Parbhoo; Finale Doshi-Velez (CoRR, 2024)

User Studies in Human-Feature-Integration. - Yixin Li; Lucas Lefebvre; Sonali Parbhoo; Finale Doshi-Velez; Isaac Lage (IUI Workshops, 2025)