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Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural
  Networks

Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks

8 October 2021
Cody Blakeney
G. Atkinson
Nathaniel Huish
Yan Yan
V. Metsis
Ziliang Zong
ArXivPDFHTML

Papers citing "Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks"

2 / 2 papers shown
Title
Beyond Accuracy: A Critical Review of Fairness in Machine Learning for
  Mobile and Wearable Computing
Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing
Sofia Yfantidou
Marios Constantinides
Dimitris Spathis
Athena Vakali
Daniele Quercia
F. Kawsar
HAI
FaML
28
18
0
27 Mar 2023
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
326
4,212
0
23 Aug 2019
1