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Measuring Model Fairness under Noisy Covariates: A Theoretical
  Perspective

Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective

20 May 2021
Flavien Prost
Pranjal Awasthi
Nicholas Blumm
A. Kumthekar
Trevor Potter
Li Wei
Xuezhi Wang
Ed H. Chi
Jilin Chen
Alex Beutel
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Papers citing "Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective"

4 / 4 papers shown
Title
Fairness in Ranking under Uncertainty
Fairness in Ranking under Uncertainty
Ashudeep Singh
David Kempe
Thorsten Joachims
28
49
0
14 Jul 2021
Evaluating Fairness of Machine Learning Models Under Uncertain and
  Incomplete Information
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Pranjal Awasthi
Alex Beutel
Matthaeus Kleindessner
Jamie Morgenstern
Xuezhi Wang
FaML
54
55
0
16 Feb 2021
False Information on Web and Social Media: A Survey
False Information on Web and Social Media: A Survey
Srijan Kumar
Neil Shah
134
346
0
23 Apr 2018
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
207
2,082
0
24 Oct 2016
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