Towards a rigorous science of interpretable machine learning F Doshi-Velez, B Kim arXiv preprint arXiv:1702.08608, 2017 | 4082 | 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 | 709 | 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 | 673 | 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 | 586 | 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 | 496 | 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 | 489 | 2014 |

Guidelines for reinforcement learning in healthcare O Gottesman, F Johansson, M Komorowski, A Faisal, D Sontag, ... Nature medicine 25 (1), 16-18, 2019 | 415 | 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 | 393 | 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 | 313 | 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 | 296 | 2018 |

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 | 277 | 2014 |

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 | 268 | 2020 |

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 | 260 | 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 | 212 | 2019 |

A Bayesian nonparametric approach to modeling motion patterns J Joseph, F Doshi-Velez, AS Huang, N Roy Autonomous Robots 31, 383-400, 2011 | 207 | 2011 |

A Bayesian nonparametric approach to modeling motion patterns J Joseph, F Doshi-Velez, AS Huang, N Roy Autonomous Robots 31, 383-400, 2011 | 207 | 2011 |

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 | 202 | 2019 |

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 | 189 | 2016 |

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 | 188 | 2018 |

Variational inference for the Indian buffet process F Doshi, K Miller, J Van Gael, YW Teh Artificial Intelligence and Statistics, 137-144, 2009 | 185 | 2009 |