A Comprehensive Empirical Study of Bias Mitigation Methods for Machine
Learning Classifiers

A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers

7 July 2022

Zhenpeng Chen

Papers citing "A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers"

7 / 7 papers shown

Title
Whence Is A Model Fair? Fixing Fairness Bugs via Propensity Score Matching Kewen Peng Yicheng Yang Hao Zhuo 32 0 0 23 Apr 2025
Understanding trade-offs in classifier bias with quality-diversity optimization: an application to talent management Catalina M Jaramillo Paul Squires Julian Togelius 78 2 0 25 Nov 2024
Bias Testing and Mitigation in LLM-based Code Generation Dong Huang Qingwen Bu Jie M. Zhang Xiaofei Xie Junjie Chen Heming Cui 48 20 0 03 Sep 2023
M $^3$ Fair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method Yinghao Zhu Jingkun An Enshen Zhou Lu An Junyi Gao ... Haoran Feng Bo-Ru Hou Wen Tang Cheng Pan Liantao Ma 9 5 0 07 Jun 2023
Causality-Aided Trade-off Analysis for Machine Learning Fairness Zhenlan Ji Pingchuan Ma Shuai Wang Yanhui Li FaML 34 7 0 22 May 2023
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 326 4,212 0 23 Aug 2019
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,087 0 24 Oct 2016