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Towards Fair Machine Learning Software: Understanding and Addressing
  Model Bias Through Counterfactual Thinking

Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking

16 February 2023
Zichong Wang
Yangze Zhou
M. Qiu
I. Haque
Laura Brown
Yi He
Jianwu Wang
David Lo
Wenbin Zhang
    FaML
ArXivPDFHTML

Papers citing "Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking"

3 / 3 papers shown
Title
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
Caleb J. S. Barr
Olivia Erdelyi
Paul D. Docherty
Randolph C. Grace
FaML
63
0
0
10 Nov 2024
Fairness in Machine Learning
Fairness in Machine Learning
L. Oneto
Silvia Chiappa
FaML
233
485
0
31 Dec 2020
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
286
4,143
0
23 Aug 2019
1