Umang Bhatt bio photo

Umang Bhatt

Assistant Professor & Faculty Fellow
New York University

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Research

My interests broadly lie in the field of trustworthy machine learning (ML). During my PhD, I focused on algorithmic transparency and its effects on AI-assisted decision-making. I am currently build tools for routing decision-makers to appropriate forms of decision support and for cataloging how AI systems are used in decision-making contexts all over the world.

You can find the most up-to-date list of my work on Google Scholar. The list below is updated periodically.

Journal and Conference Publications (Refereed and Archived)

  1. Katherine Collins*, Ilia Sucholutsky*, Umang Bhatt*, Kartik Chandra*, Lionel Wong*, Mina Lee, Cedegao Zhang, Tan Zhi-Xuan, Mark Ho, Vikash Mansinghka, Adrian Weller, Joshua Tenenbaum, Thomas Griffiths
    Building Machines that Learn and Think with People
    Nature Human Behavior, 2024.

  2. Umang Bhatt*, Holli Sargeant*
    When Should Algorithms Resign? A Proposal for AI Governance
    IEEE Computer, 2024.

  3. Katherine Collins, Albert Jiang, Simon Frieder, Lionel Wong, Miri Zilka, Umang Bhatt, Thomas Lukasiewicz, Yuhuai Wu, Joshua Tenenbaum, William Hart, Timothy Gowers, Wenda Li, Adrian Weller, Mateja Jamnik
    Evaluating Language Models for Mathematics through Interactionse
    Proceedings of the National Academy of Sciences, 2024.

  4. Sreejan Kumar, Raja Marjieh, Byron Zhang, Declan Campbell, Michael Hu, Umang Bhatt, Brenden Lake, Thomas Griffiths
    Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction
    46th Annual Conference of the Cognitive Science Society (CogSci), 2024.

  5. Valerie Chen*, Umang Bhatt*, Hoda Heidari, Adrian Weller, Ameet Talwalkar
    Perspectives on Incorporating Expert Feedback into Model Updates
    Patterns, 2023.

  6. Isa Inuwa-Dutse, Alice Toniolo, Adrian Weller, Umang Bhatt
    Algorithmic Loafing and Mitigation Strategies in Human-AI Teams
    Computers in Human Behavior: Artificial Humans, 2023.

  7. Matthew Barker, Katherine Collins, Krishnamurthy Dvijotham, Adrian Weller, Umang Bhatt
    Selective Concept Models: Permitting Stakeholder Customisation at Test-Time
    AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2023.

  8. Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine Collins, Ashley Casovan, Adrian Weller, Ameet Talwalkar, Valerie Chen, Umang Bhatt
    FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipeline
    ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2023.

  9. Alan Chan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, Zhonghao He, Yawen Duan, Micah Carroll, Michelle Lin, Alex Mayhew, Katherine Collins, Maryam Molamohammadi, John Burden, Wanru Zhao, Shalaleh Rismani, Konstantinos Voudouris, Umang Bhatt, Adrian Weller, David Krueger, Tegan Maharaj
    Harms from Increasingly Agentic Algorithmic Systems
    ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2023.

  10. Katherine Collins, Matthew Barker, Mateo Espinosa Zarlenga, Naveen Raman, Umang Bhatt, Mateja Jamnik, Ilia Sucholutsky, Adrian Weller, Krishnamurthy Dvijotham
    Human Uncertainty in Concept-Based AI Systems
    AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2023.

  11. Zeju Qiu, Weiyang Liu, Tim Xiao, Zhen Liu, Yucen Luo, Umang Bhatt, Adrian Weller, Bernhard Scholkopf
    Iterative Teaching by Data Hallucination
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.

  12. Javier Abad Martinez, Umang Bhatt, Adrian Weller, Giovanni Cherubin
    Approximating Full Conformal Prediction at Scale via Influence Functions
    AAAI Conference on Artificial Intelligence (AAAI), 2023.

  13. Mateo Zarlenga, Pietro Barbiero, Zohreh Shams, Dmitry Kazhdan, Umang Bhatt, Adrian Weller, Mateja Jamnik
    Towards Robust Metrics for Concept Representation Evaluation
    AAAI Conference on Artificial Intelligence (AAAI), 2023.

  14. Katherine Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley Love, Adrian Weller
    Human-in-the-Loop Mixup
    Conference on Uncertainty in Artificial Intelligence (UAI), 2023. (Oral)

  15. Ilia Sucholutsky, Ruairidh Battleday, Katherine Collins, Raja Marjieh, Joshua Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas Griffiths
    On the Informativeness of Supervision Signals
    Conference on Uncertainty in Artificial Intelligence (UAI), 2023.

  16. John Zerilli, Umang Bhatt, Adrian Weller
    How Transparency Modulates Trust in Artificial Intelligence
    Patterns, 2022.

  17. Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency
    Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
    Findings of the Association for Computational Linguistics (EMNLP), 2022.

  18. Katherine Collins*, Umang Bhatt*, Adrian Weller
    Eliciting and Learning with Soft Labels from Every Annotator
    AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2022.

  19. Varun Babbar, Umang Bhatt, Adrian Weller
    On the Utility of Prediction Sets in Human-AI Teams
    International Joint Conference on Artificial Intelligence (IJCAI), 2022. (Oral)

  20. Dan Ley, Umang Bhatt, Adrian Weller
    Diverse and Amortised Counterfactual Explanations for Uncertainty Estimates
    AAAI Conference on Artificial Intelligence (AAAI), 2022.

  21. Julius von Kugelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Scholkopf
    On the Fairness of Causal Algorithmic Recourse
    AAAI Conference on Artificial Intelligence (AAAI), 2022. (Oral)

  22. Umang Bhatt, Javier Antoran, Yunfeng Zhang, Q. Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Melancon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, Lama Nachman, Rumi Chunara, Madhulika Srikumar, Adrian Weller, Alice Xiang
    Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
    AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2021.

  23. Javier Antoran, Umang Bhatt, Tameem Adel, Adrian Weller, Jose Miguel Hernandez-Lobato
    Getting a CLUE: A Method for Explaining Uncertainty Estimates
    International Conference on Learning Representations (ICLR), 2021. (Oral)

  24. Matt Chapman-Rounds, Umang Bhatt, Erik Pazos, Marc-Andre Schulz, Konstantinos Georgatzis
    FIMAP: Feature Importance by Minimal Adversarial Perturbation
    AAAI Conference on Artificial Intelligence (AAAI), 2021.

  25. Umang Bhatt, Adrian Weller, José M. F. Moura
    Evaluating and Aggregating Feature-based Model Explanations
    International Joint Conference on Artificial Intelligence (IJCAI), 2020.

  26. Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, Peter Eckersley
    Explainable Machine Learning in Deployment
    ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2020.

  27. Botty Dimanov, Umang Bhatt, Mateja Jamnik, Adrian Weller
    You shouldn’t trust me: Learning models which conceal unfairness from multiple explanation methods
    European Conference on Artificial Intelligence (ECAI), 2020.

  28. Brian Davis*, Umang Bhatt*, Kartikeya Bhardwaj*, Radu Marculescu, José M. F. Moura
    On Network Science and Mutual Information for Explaining Deep Neural Networks
    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020.

  29. Aaron Roth, Samantha Reig, Umang Bhatt, Jonathan Shulgach, Tamara Amin, Afsaneh Doryab, Fei Fang, Manuela Veloso
    A Robot’s Expressive Language Affects Human Strategy and Perceptions in a Competitive Game
    IEEE International Conference on Robot and Human Interactive Communication (ROMAN), 2019.

  30. Umang Bhatt*, Edgar Xi*, Shouvik Mani*, Zico Kolter
    Intelligent Pothole Detection and Road Condition Assessment
    Bloomberg Data for Good Exchange (D4GX), 2017.
    Talk at UChicago Data Science for Social Good (DSSG), 2017.

Theses

  1. Umang Bhatt
    Trustworthy Machine Learning: From Algorithmic Transparency to Decision Support
    Universty of Cambridge, 2024.

Book Chapters (Refereed and Archived)

  1. Umang Bhatt, Zohreh Shams
    Trust in Artificial Intelligence: Clinicians Are Essential
    Healthcare Information Technology for Cardiovascular Medicine, 2021.

Workshop Publications (Refereed) – Not Updated Frequently

  1. Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa, Vera Liao, Himani Agrawal, Umang Bhatt, Krishnaram Kenthapadi, Alice Xiang, Maarten de Rijke, Nicholas Drabowski
    Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
    ICML Workshop on Interpretable ML in Healthcare, 2022.

  2. Valerie Chen*, Umang Bhatt*, Hoda Heidari, Adrian Weller, Ameet Talwalkar
    Perspectives on Incorporating Expert Feedback into Model Updates
    ICML Workshop on Updatable Machine Learning, 2022.

  3. Umang Bhatt, Adrian Weller, Giovanni Cherubin
    Fast Conformal Classification using Influence Functions
    10th Symposium on Conformal and Probabilistic Prediction with Applications (COPA), 2021.

  4. Dan Ley, Umang Bhatt, Adrian Weller
    Diverse and Amortised Counterfactual Explanations for Uncertainty Estimates
    ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI, 2021.

  5. Andrei Margeloiu*, Matthew Ashman*, Umang Bhatt*, Yanzhi Chen, Mateja Jamnik, Adrian Weller
    Do Concept Bottleneck Models Learn As Intended?
    ICLR Workshop on Responsible AI, 2021.

  6. Dan Ley, Umang Bhatt, Adrian Weller
    δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimates
    ICLR Workshop on Security and Safety in Machine Learning Systems, 2021.

  7. Ana Lucic, Madhulika Srikumar, Umang Bhatt, Alice Xiang, Ankur Taly, Q. Vera Liao, Maarten de Rijke
    A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
    CHI Workshop on Operationalizing Human-centered Perspectives in Explainable AI, 2021.

  8. Torgyn Shaikhina*, Umang Bhatt*, Roxanne Zhang, Konstantinos Georgatzis, Alice Xiang, Adrian Weller
    Effects of Uncertainty on the Quality of Feature Importance Estimates
    AAAI Workshop on Explainable Agency in Artificial Intelligence, 2021.

  9. Julius von Kugelgen, Umang Bhatt, Amir-Hossein Karimi, Isabel Valera, Adrian Weller, Bernhard Scholkopf
    On the Fairness of Causal Algorithmic Recourse
    NeurIPS Workshop on Algorithmic Fairness through the Lens of Causality and Interpretability, 2020.

  10. Umang Bhatt, McKane Andrus, Adrian Weller, Alice Xiang
    Machine Learning Explainability for External Stakeholders
    ICML Workshop on Extending Explainable AI: Beyond Deep Models and Classifiers, 2020.
    ICML Workshop on Human Interpretability, 2020. (Spotlight)
    IJCAI Workshop on Explainable AI, 2020.

  11. Javier Antoran, Umang Bhatt, Jose Miguel Hernandez-Lobato, Adrian Weller, Tameem Adel
    Getting a CLUE: A Method for Explaining Uncertainty Estimates
    ICLR Workshop on Machine Learning in Real Life (ML-IRL), 2020. (Oral)

  12. Umang Bhatt, Adrian Weller, Muhammad Bilal Zafar, Krishna Gummadi
    Counterfactual Accuracies of Alternative Models
    ICLR Workshop on Machine Learning in Real Life (ML-IRL), 2020.

  13. Botty Dimanov, Umang Bhatt, Mateja Jamnik, Adrian Weller
    Models can be learned to conceal unfairness from explanation methods
    AAAI Workshop on Safe AI, 2020. (Oral)

  14. Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, Peter Eckersley
    Explainable Machine Learning in Deployment
    NeurIPS Workshop on Human-Centric Machine Learning (HCML), 2019.

  15. Umang Bhatt, Brian Davis, José M. F. Moura
    Diagnostic Model Explanations: A Medical Narrative
    AAAI Spring Symposium on Interpretable AI for Well-being, 2019.
    Best Paper Award

  16. Brian Davis*, Umang Bhatt*, Kartikeya Bhardwaj*, Radu Marculescu, José M. F. Moura
    NIF: A Framework for Quantifying Neural Information Flow in Deep Networks
    AAAI Workshop on Network Interpretability for Deep Learning, 2019.

  17. Umang Bhatt, Pradeep Ravikumar, José M. F. Moura
    Towards Aggregating Weighted Feature Attributions
    AAAI Workshop on Network Interpretability for Deep Learning, 2019.

  18. Umang Bhatt, Pradeep Ravikumar, José M. F. Moura
    Building Human-Machine Trust via Interpretability
    AAAI Conference on Artificial Intelligence (AAAI), 2019. (Student Abstract)

  19. Aaron Roth*, Umang Bhatt*, Tamara Amin*, Afsaneh Doryab, Fei Fang, Manuela Veloso
    The Impact of Humanoid Affect Expression on Human Behavior in Game-Theoretic Setting
    IJCAI Workshop on Humanizing Artificial Intelligence, 2018. (Oral)

  20. Umang Bhatt
    Maintaining the Humanity of Our Models
    AAAI Spring Symposium on AI and Society: Ethics, Safety and Trustworthiness in Intelligent Agents, 2018.