29 citations · 75 across the 12 of their papers we have counts for
4 papers · 1 filter
Software Fairness Dilemma: Is Bias Mitigation a Zero-Sum Game?
Zhenpeng Chen, Xinyue Li, Jie M. Zhang +5
Fairness is a critical requirement for Machine Learning (ML) software, driving the development of numerous bias mitigation methods. Previous research has identified a leveling-down…
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
Zhenpeng Chen, Xinyue Li, Jie M. Zhang +2
Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes. This paper intro…
Fairness Improvement with Multiple Protected Attributes: How Far Are We?
Zhenpeng Chen, Jie M. Zhang, Federica Sarro +1
Existing research mostly improves the fairness of Machine Learning (ML) software regarding a single protected attribute at a time, but this is unrealistic given that many users hav…
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Max Hort, Zhenpeng Chen, Jie M. Zhang +2
This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias…