Umang Bhatt bio photo

Umang Bhatt

PhD Candidate
University of Cambridge
Cambridge, England, UK

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Research

My interests broadly lie in the field of trustworthy machine learning (ML). Specifically, I focus on algorithmic transparency and its effects on decision-making. I study how to create ML systems that explain their predictions to stakeholders, leverage stakeholder expertise for better human-machine team performance, and interact with stakeholders to account for their goals and values.

Conference Publications (Refereed and Archived)

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

  2. 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.

  3. 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.

  4. 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)

  5. 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.

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

  7. 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 (FAT*), 2020.

  8. 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.

  9. 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.

  10. 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.

  11. 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.


Journal Papers and Book Chapters (Refereed and Archived)

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

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


Workshop Publications (Refereed)

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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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)

  10. 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.

  11. 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)

  12. 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.

  13. 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

  14. 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.

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

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

  17. 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)

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