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Metrics and methods for a systematic comparison of fairness-aware
  machine learning algorithms

Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms

8 October 2020
Gareth Jones
James M. Hickey
Pietro G. Di Stefano
C. Dhanjal
Laura C. Stoddart
V. Vasileiou
    FaML
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Papers citing "Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms"

3 / 3 papers shown
Title
Fair Enough: Searching for Sufficient Measures of Fairness
Fair Enough: Searching for Sufficient Measures of Fairness
Suvodeep Majumder
Joymallya Chakraborty
Gina R. Bai
Kathryn T. Stolee
Tim Menzies
11
26
0
25 Oct 2021
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
296
4,203
0
23 Aug 2019
Fair prediction with disparate impact: A study of bias in recidivism
  prediction instruments
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
192
2,082
0
24 Oct 2016
1