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Fair When Trained, Unfair When Deployed: Observable Fairness Measures
  are Unstable in Performative Prediction Settings

Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings

10 February 2022
Alan Mishler
Niccolò Dalmasso
ArXivPDFHTML

Papers citing "Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings"

3 / 3 papers shown
Title
Fair Wasserstein Coresets
Fair Wasserstein Coresets
Zikai Xiong
Niccolò Dalmasso
Shubham Sharma
Freddy Lecue
Daniele Magazzeni
Vamsi K. Potluru
T. Balch
Manuela Veloso
34
2
0
09 Nov 2023
Improving Fair Training under Correlation Shifts
Improving Fair Training under Correlation Shifts
Yuji Roh
Kangwook Lee
Steven Euijong Whang
Changho Suh
27
17
0
05 Feb 2023
Prisoners of Their Own Devices: How Models Induce Data Bias in
  Performative Prediction
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction
José P. Pombal
Pedro Saleiro
Mário A. T. Figueiredo
P. Bizarro
23
4
0
27 Jun 2022
1