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One-vs.-One Mitigation of Intersectional Bias: A General Method to Extend Fairness-Aware Binary Classification

One-vs.-One Mitigation of Intersectional Bias: A General Method to Extend Fairness-Aware Binary Classification

26 October 2020
Kenji Kobayashi
Yuri Nakao
    FaML
ArXivPDFHTML

Papers citing "One-vs.-One Mitigation of Intersectional Bias: A General Method to Extend Fairness-Aware Binary Classification"

3 / 3 papers shown
Title
Revisiting Technical Bias Mitigation Strategies
Revisiting Technical Bias Mitigation Strategies
Abdoul Jalil Djiberou Mahamadou
Artem A. Trotsyuk
26
0
0
22 Oct 2024
Factoring the Matrix of Domination: A Critical Review and Reimagination
  of Intersectionality in AI Fairness
Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness
Anaelia Ovalle
Arjun Subramonian
Vagrant Gautam
Gilbert Gee
Kai-Wei Chang
22
36
0
16 Mar 2023
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Max Hort
Zhenpeng Chen
Jie M. Zhang
Mark Harman
Federica Sarro
FaML
AI4CE
31
159
0
14 Jul 2022
1