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Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce
  Discrimination

Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination

25 September 2020
Tao Zhang
Tianqing Zhu
Jing Li
Mengde Han
Wanlei Zhou
Philip S. Yu
    FaML
ArXivPDFHTML

Papers citing "Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination"

12 / 12 papers shown
Title
Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers
Huan Tian
Guangsheng Zhang
Bo Liu
Tianqing Zhu
Ming Ding
Wanlei Zhou
53
0
0
08 Mar 2025
Alpha and Prejudice: Improving $α$-sized Worst-case Fairness via
  Intrinsic Reweighting
Alpha and Prejudice: Improving ααα-sized Worst-case Fairness via Intrinsic Reweighting
Jing Li
Yinghua Yao
Yuangang Pan
Xuanqian Wang
Ivor Tsang
Xiuju Fu
FaML
37
0
0
05 Nov 2024
A Catalog of Fairness-Aware Practices in Machine Learning Engineering
A Catalog of Fairness-Aware Practices in Machine Learning Engineering
Gianmario Voria
Giulia Sellitto
Carmine Ferrara
Francesco Abate
A. Lucia
F. Ferrucci
Gemma Catolino
Fabio Palomba
FaML
39
3
0
29 Aug 2024
A novel approach for Fair Principal Component Analysis based on
  eigendecomposition
A novel approach for Fair Principal Component Analysis based on eigendecomposition
G. D. Pelegrina
L. Duarte
FaML
22
11
0
24 Aug 2022
Transferring Fairness under Distribution Shifts via Fair Consistency
  Regularization
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
Bang An
Zora Che
Mucong Ding
Furong Huang
14
31
0
26 Jun 2022
Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision
  Making
Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making
Miriam Rateike
Ayan Majumdar
Olga Mineeva
Krishna P. Gummadi
Isabel Valera
OffRL
32
11
0
10 May 2022
Modeling Techniques for Machine Learning Fairness: A Survey
Modeling Techniques for Machine Learning Fairness: A Survey
Mingyang Wan
Daochen Zha
Ninghao Liu
Na Zou
SyDa
FaML
30
36
0
04 Nov 2021
Fair-SSL: Building fair ML Software with less data
Fair-SSL: Building fair ML Software with less data
Joymallya Chakraborty
Suvodeep Majumder
Huy Tu
SyDa
11
5
0
03 Nov 2021
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data
  and Bayesian Inference
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
Disi Ji
Padhraic Smyth
M. Steyvers
34
44
0
19 Oct 2020
More Than Privacy: Applying Differential Privacy in Key Areas of
  Artificial Intelligence
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
Tianqing Zhu
Dayong Ye
Wei Wang
Wanlei Zhou
Philip S. Yu
SyDa
34
125
0
05 Aug 2020
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
233
674
0
17 Feb 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
2,084
0
24 Oct 2016
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