World's Best Scientists 2026 revealed!
Finale Doshi-Velez

Finale Doshi-Velez

D-Index & Metrics

Computer Science

D-Index
50
Citations
12910
World Ranking
5530
National Ranking
2526

Research.com Recognitions

  • 2018 - Fellow of Alfred P. Sloan Foundation

Overview

Finale Doshi-Velez is affiliated with Harvard University in the United States. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence. They have produced 137 publications in this domain, emphasizing specialized subfields such as Epidemiology, Pharmacology, Economics and Econometrics, and Management Science and Operations Research.

Their work engages a range of topics including Explainable Artificial Intelligence (XAI), Machine Learning in Healthcare, Adversarial Robustness in Machine Learning, Sepsis Diagnosis and Treatment, Treatment of Major Depression, Reinforcement Learning in Robotics, and Anomaly Detection Techniques and Applications.

Frequent collaborators include Weiwei Pan, Sonali Parbhoo, Susan A. Murphy, Roy H. Perlis, and Yaniv Yacoby, with co-authorship counts of 22, 17, 15, 11, and 11 respectively.

The scientist's publications are often featured in venues such as arXiv (Cornell University), PubMed, Proceedings of the AAAI Conference on Artificial Intelligence, Journal of Affective Disorders, and The Lancet Digital Health. They have notably contributed 73 papers to arXiv, followed by 8 in PubMed, 4 in AAAI conferences, 3 in the Journal of Affective Disorders, and 2 in The Lancet Digital Health.

Recent papers include:

  • "The myth of generalisability in clinical research and machine learning in health care" (2020, The Lancet Digital Health)
  • "How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection" (2021, Translational Psychiatry)
  • "Ethical and regulatory challenges of large language models in medicine" (2024, The Lancet Digital Health)
  • "Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report" (2022, arXiv (Cornell University))
  • "Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers" (2023, Cell Reports Medicine)

Finale Doshi-Velez was awarded the status of Fellow of the Alfred P. Sloan Foundation in 2018.

Best Publications

  • Towards A Rigorous Science of Interpretable Machine Learning

    Finale Doshi-Velez;Been Kim

  • Do no harm: a roadmap for responsible machine learning for health care.

    Jenna Wiens;Suchi Saria;Mark Sendak;Marzyeh Ghassemi

  • Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients

    Andrew Slavin Ross;Finale Doshi-Velez

  • Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations.

    Andrew Slavin Ross;Michael C. Hughes;Finale Doshi-Velez

  • The myth of generalisability in clinical research and machine learning in health care.

    Joseph Futoma;Morgan Simons;Trishan Panch;Finale Doshi-Velez

  • Guidelines for reinforcement learning in healthcare

    Omer Gottesman;Fredrik Johansson;Matthieu Komorowski;Aldo Faisal

  • Unfolding physiological state: mortality modelling in intensive care units

    Marzyeh Ghassemi;Tristan Naumann;Finale Doshi-Velez;Nicole Brimmer

  • Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

    Mike Wu;Michael C. Hughes;Sonali Parbhoo;Maurizio Zazzi

  • A Bayesian framework for learning rule sets for interpretable classification

    Tong Wang;Cynthia Rudin;Finale Doshi-Velez;Yimin Liu

  • Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

    Stefan Depeweg;José Miguel Hernández-Lobato;Finale Doshi-Velez;Steffen Udluft

  • How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection.

    Maia Jacobs;Melanie F. Pradier;Thomas H. McCoy;Roy H. Perlis

  • A Bayesian nonparametric approach to modeling motion patterns

    Joshua Joseph;Finale Doshi-Velez;Albert S. Huang;Nicholas Roy

  • Considerations for Evaluation and Generalization in Interpretable Machine Learning

    Finale Doshi-Velez;Been Kim

  • Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens

    Maia Jacobs;Jeffrey He;Melanie F. Pradier;Barbara Lam

  • An Evaluation of the Human-Interpretability of Explanation

    Isaac Lage;Emily Chen;Jeffrey He;Menaka Narayanan

  • The Infinite Partially Observable Markov Decision Process

    Finale Doshi-velez

  • Learning and policy search in stochastic dynamical systems with Bayesian neural networks

    Stefan Depeweg;José Miguel Hernández-Lobato;Finale Doshi-Velez;Steffen Udluft

  • How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation.

    Menaka Narayanan;Emily Chen;Jeffrey He;Been Kim

  • A Roadmap for a Rigorous Science of Interpretability.

    Finale Doshi-Velez;Been Kim

  • Evaluating Reinforcement Learning Algorithms in Observational Health Settings

    Omer Gottesman;Fredrik D. Johansson;Joshua Meier;Jack Dent

  • Accountability of AI Under the Law: The Role of Explanation

    Finale Doshi-Velez;Mason A. Kortz

Frequent Co-Authors

Emma Brunskill
Emma Brunskill Stanford University
Been Kim
Been Kim Google (United States)
Maurizio Zazzi
Maurizio Zazzi University of Siena
George Konidaris
George Konidaris Brown University
Volker Roth
Volker Roth University of Basel
Isaac S. Kohane
Isaac S. Kohane Harvard University
José Miguel Hernández-Lobato
José Miguel Hernández-Lobato University of Cambridge
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge

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