Wasserstein-based fairness interpretability framework for machine learning models

6 November 2020

Papers citing "Wasserstein-based fairness interpretability framework for machine learning models"

6 / 6 papers shown

Title
Enforcing Fairness Where It Matters: An Approach Based on Difference-of-Convex Constraints Yutian He Yankun Huang Yao Yao Qihang Lin FaML 51 0 0 18 May 2025
True to the Model or True to the Data? Hugh Chen Joseph D. Janizek Scott M. Lundberg Su-In Lee TDI FAtt 147 166 0 29 Jun 2020
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds M. Kovalev Lev V. Utkin AAML 68 31 0 05 May 2020
Feature relevance quantification in explainable AI: A causal problem Dominik Janzing Lenon Minorics Patrick Blobaum FAtt CML 68 280 0 29 Oct 2019
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 222 4,312 0 07 Oct 2016
Certifying and removing disparate impact Michael Feldman Sorelle A. Friedler John Moeller C. Scheidegger Suresh Venkatasubramanian FaML 194 1,986 0 11 Dec 2014