Finale Doshi-Velez
Finale Doshi-Velez
Professor, Harvard
Verified email at - Homepage
Cited by
Cited by
Towards a rigorous science of interpretable machine learning
F Doshi-Velez, B Kim
arXiv preprint arXiv:1702.08608, 2017
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
A Ross, F Doshi-Velez
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
Do no harm: a roadmap for responsible machine learning for health care
J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, ...
Nature medicine 25 (9), 1337-1340, 2019
Right for the right reasons: Training differentiable models by constraining their explanations
AS Ross, MC Hughes, F Doshi-Velez
arXiv preprint arXiv:1703.03717, 2017
Accountability of AI under the law: The role of explanation
F Doshi-Velez, M Kortz, R Budish, C Bavitz, S Gershman, D O'Brien, ...
arXiv preprint arXiv:1711.01134, 2017
Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis
F Doshi-Velez, Y Ge, I Kohane
Pediatrics 133 (1), e54-e63, 2014
Guidelines for reinforcement learning in healthcare
O Gottesman, F Johansson, M Komorowski, A Faisal, D Sontag, ...
Nature medicine 25 (1), 16-18, 2019
Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning
S Depeweg, JM Hernandez-Lobato, F Doshi-Velez, S Udluft
International conference on machine learning, 1184-1193, 2018
A bayesian framework for learning rule sets for interpretable classification
T Wang, C Rudin, F Doshi-Velez, Y Liu, E Klampfl, P MacNeille
Journal of Machine Learning Research 18 (70), 1-37, 2017
Beyond sparsity: Tree regularization of deep models for interpretability
M Wu, M Hughes, S Parbhoo, M Zazzi, V Roth, F Doshi-Velez
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
The myth of generalisability in clinical research and machine learning in health care
J Futoma, M Simons, T Panch, F Doshi-Velez, LA Celi
The Lancet Digital Health 2 (9), e489-e492, 2020
Unfolding physiological state: Mortality modelling in intensive care units
M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, ...
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
How do humans understand explanations from machine learning systems? an evaluation of the human-interpretability of explanation
M Narayanan, E Chen, J He, B Kim, S Gershman, F Doshi-Velez
arXiv preprint arXiv:1802.00682, 2018
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
Explainable reinforcement learning via reward decomposition
Z Juozapaitis, A Koul, A Fern, M Erwig, F Doshi-Velez
IJCAI/ECAI Workshop on explainable artificial intelligence, 2019
A Bayesian nonparametric approach to modeling motion patterns
J Joseph, F Doshi-Velez, AS Huang, N Roy
Autonomous Robots 31, 383-400, 2011
Considerations for evaluation and generalization in interpretable machine learning
F Doshi-Velez, B Kim
Explainable and interpretable models in computer vision and machine learning …, 2018
Learning and policy search in stochastic dynamical systems with bayesian neural networks
S Depeweg, JM Hernández-Lobato, F Doshi-Velez, S Udluft
arXiv preprint arXiv:1605.07127, 2016
How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection
M Jacobs, MF Pradier, TH McCoy Jr, RH Perlis, F Doshi-Velez, KZ Gajos
Translational psychiatry 11 (1), 108, 2021
Gathering strength, gathering storms: The one hundred year study on artificial intelligence (AI100) 2021 study panel report
ML Littman, I Ajunwa, G Berger, C Boutilier, M Currie, F Doshi-Velez, ...
arXiv preprint arXiv:2210.15767, 2022
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