I. Elizabeth Kumar
I. Elizabeth Kumar
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Problems with Shapley-value-based explanations as feature importance measures
IE Kumar, S Venkatasubramanian, C Scheidegger, S Friedler
International Conference on Machine Learning, 5491-5500, 2020
The fallacy of AI functionality
ID Raji, IE Kumar, A Horowitz, A Selbst
2022 ACM Conference on Fairness, Accountability, and Transparency, 959-972, 2022
Shapley Residuals: Quantifying the limits of the Shapley value for explanations
IE Kumar, C Scheidegger, S Venkatasubramanian, S Friedler
Advances in Neural Information Processing Systems 34, 26598-26608, 2021
Epistemic values in feature importance methods: Lessons from feminist epistemology
L Hancox-Li, IE Kumar
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021
Equalizing credit opportunity in algorithms: Aligning algorithmic fairness research with US fair lending regulation
IE Kumar, KE Hines, JP Dickerson
Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 357-368, 2022
Deconstructing design decisions: Why courts must interrogate machine learning and other technologies
AD Selbst, S Venkatasubramanian, IE Kumar
Ohio State Law Journal, 23-22, 2024
To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models
C Cousins, IE Kumar, S Venkatasubramanian
International Conference on Artificial Intelligence and Statistics, 4573-4581, 2024
Systems and methods for risk factor predictive modeling with model explanations
M Maier, S Li, H Carlotto, I Kumar
US Patent 11,710,564, 2023
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