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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
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Papers citing "A Survey on Bias and Fairness in Machine Learning"

4 / 1,604 papers shown
Title
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks
R. Confalonieri
Tillman Weyde
Tarek R. Besold
Fermín Moscoso del Prado Martín
12
24
0
19 Jun 2019
Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers
Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers
Baihan Lin
13
2
0
27 Feb 2019
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
A. Chaney
Brandon M Stewart
Barbara E. Engelhardt
CML
169
312
0
30 Oct 2017
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
201
2,082
0
24 Oct 2016