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On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach


Nov 02, 2022
Dennis Wei, Rahul Nair, Amit Dhurandhar, Kush R. Varshney, Elizabeth M. Daly, Moninder Singh

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* Published at NeurIPS 2022 

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Anomaly Attribution with Likelihood Compensation


Aug 23, 2022
Tsuyoshi Idé, Amit Dhurandhar, Jiří Navrátil, Moninder Singh, Naoki Abe

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* 8 pages, 7 figures 

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Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations


May 08, 2022
Hammaad Adam, Ming Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi

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* Accepted to the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES '22), ACM, Oxford, UK, 2022 

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Ground-Truth, Whose Truth? -- Examining the Challenges with Annotating Toxic Text Datasets


Dec 07, 2021
Kofi Arhin, Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Moninder Singh

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* 15 pages 

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An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness


Sep 29, 2021
Moninder Singh, Gevorg Ghalachyan, Kush R. Varshney, Reginald E. Bryant

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* presented at the 2021 KDD Workshop on Measures and Best Practices for Responsible AI 

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AI Explainability 360: Impact and Design


Sep 24, 2021
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

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* arXiv admin note: text overlap with arXiv:1909.03012 

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Your fairness may vary: Group fairness of pretrained language models in toxic text classification


Aug 03, 2021
Ioana Baldini, Dennis Wei, Karthikeyan Natesan Ramamurthy, Mikhail Yurochkin, Moninder Singh

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Understanding racial bias in health using the Medical Expenditure Panel Survey data


Nov 04, 2019
Moninder Singh, Karthikeyan Natesan Ramamurthy

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* 8 pages, 8 tables 

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One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques


Sep 14, 2019
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

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