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
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
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
The fallacy of AI functionality
ID Raji, IE Kumar, A Horowitz, A Selbst
2022 ACM Conference on Fairness, Accountability, and Transparency, 959-972, 2022
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
The Legal Construction of Black Boxes
AD Selbst, S Venkatasubramanian, IE Kumar
We Robot, 2021
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