An evaluation of the human-interpretability of explanation I Lage, E Chen, J He, M Narayanan, B Kim, S Gershman, F Doshi-Velez arXiv preprint arXiv:1902.00006, 2019 | 215 | 2019 |
Human-in-the-loop interpretability prior I Lage, A Ross, SJ Gershman, B Kim, F Doshi-Velez Advances in neural information processing systems 31, 2018 | 161 | 2018 |
Human evaluation of models built for interpretability I Lage, E Chen, J He, M Narayanan, B Kim, SJ Gershman, F Doshi-Velez Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 …, 2019 | 122 | 2019 |
Evaluating reinforcement learning algorithms in observational health settings O Gottesman, F Johansson, J Meier, J Dent, D Lee, S Srinivasan, L Zhang, ... arXiv preprint arXiv:1805.12298, 2018 | 122 | 2018 |
Exploring computational user models for agent policy summarization I Lage, D Lifschitz, F Doshi-Velez, O Amir IJCAI: proceedings of the conference 28, 1401, 2019 | 78 | 2019 |
Promises and pitfalls of black-box concept learning models A Mahinpei, J Clark, I Lage, F Doshi-Velez, W Pan arXiv preprint arXiv:2106.13314, 2021 | 64 | 2021 |
The neural lasso: Local linear sparsity for interpretable explanations A Ross, I Lage, F Doshi-Velez Workshop on Transparent and Interpretable Machine Learning in Safety …, 2017 | 25 | 2017 |
Learning interpretable concept-based models with human feedback I Lage, F Doshi-Velez arXiv preprint arXiv:2012.02898, 2020 | 21 | 2020 |
When does uncertainty matter?: Understanding the impact of predictive uncertainty in ML assisted decision making S McGrath, P Mehta, A Zytek, I Lage, H Lakkaraju arXiv preprint arXiv:2011.06167, 2020 | 16 | 2020 |
Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records I Lage, TH McCoy Jr, RH Perlis, F Doshi-Velez Journal of affective disorders 306, 254-259, 2022 | 13 | 2022 |
Toward robust policy summarization I Lage, D Lifschitz, F Doshi-Velez, O Amir Autonomous agents and multi-agent systems 2019, 2081, 2019 | 11 | 2019 |
Human-in-the-loop learning of interpretable and intuitive representations I Lage, F Doshi-Velez Proceedings of the ICML Workshop on Human Interpretability in Machine …, 2020 | 10 | 2020 |
When does uncertainty matter S McGrath, P Mehta, A Zytek, I Lage, H Lakkaraju Understanding the impact of predictive uncertainty in ML assisted decision …, 2020 | 8 | 2020 |
L. wei H O Gottesman, F Johansson, J Meier, J Dent, D Lee, S Srinivasan, L Zhang, ... Lehman, M. Komorowski, M. Komorowski, A. Faisal, LA Celi, D. Sontag, and F …, 2018 | 7 | 2018 |
An evaluation of the human-interpretability of explanation. 2019 I Lage, E Chen, J He, M Narayanan, B Kim, S Gershman, F Doshi-Velez CoRR abs/1902.00006.[Europe PMC free article], 1902 | 7 | 1902 |
Li wei H. Lehman, Matthieu Komorowski, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, and Finale Doshi-Velez. Evaluating reinforcement learning algorithms in … O Gottesman, F Johansson, J Meier, J Dent, D Lee, S Srinivasan, L Zhang, ... arXiv preprint arXiv:1805.12298, 2018 | 5 | 2018 |
Do clinicians follow heuristics in prescribing antidepressants? I Lage, MF Pradier, TH McCoy Jr, RH Perlis, F Doshi-Velez Journal of Affective Disorders 311, 110-114, 2022 | 4 | 2022 |
Learning Human Proxy Functions to Optimize Machine Learning Systems for Sociotechnical Context IL Lage Harvard University, 2023 | | 2023 |
(When) Are Contrastive Explanations of Reinforcement Learning Helpful? S Narayanan, I Lage, F Doshi-Velez arXiv preprint arXiv:2211.07719, 2022 | | 2022 |
Leveraging Human Features at Test-Time I Lage, S Parbhoo, F Doshi-Velez | | 2022 |