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Group-based Fair Learning Leads to Counter-intuitive Predictions

Group-based Fair Learning Leads to Counter-intuitive Predictions

Ofir Nachum
Heinrich Jiang
    FaML
ArXiv (abs)PDFHTML

Papers citing "Group-based Fair Learning Leads to Counter-intuitive Predictions"

7 / 7 papers shown
Title
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Andrew Cotter
Heinrich Jiang
S. Wang
Taman Narayan
Maya R. Gupta
Seungil You
Karthik Sridharan
79
157
0
11 Sep 2018
A Reductions Approach to Fair Classification
A Reductions Approach to Fair Classification
Alekh Agarwal
A. Beygelzimer
Miroslav Dudík
John Langford
Hanna M. Wallach
FaML
227
1,102
0
06 Mar 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
300
2,120
0
24 Oct 2016
Equality of Opportunity in Supervised Learning
Equality of Opportunity in Supervised Learning
Moritz Hardt
Eric Price
Nathan Srebro
FaML
230
4,329
0
07 Oct 2016
Inherent Trade-Offs in the Fair Determination of Risk Scores
Inherent Trade-Offs in the Fair Determination of Risk Scores
Jon M. Kleinberg
S. Mullainathan
Manish Raghavan
FaML
121
1,775
0
19 Sep 2016
Satisfying Real-world Goals with Dataset Constraints
Satisfying Real-world Goals with Dataset Constraints
Gabriel Goh
Andrew Cotter
Maya R. Gupta
M. Friedlander
OffRL
60
215
0
24 Jun 2016
Certifying and removing disparate impact
Certifying and removing disparate impact
Michael Feldman
Sorelle A. Friedler
John Moeller
C. Scheidegger
Suresh Venkatasubramanian
FaML
201
1,992
0
11 Dec 2014