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We study the problem of learning classifiers with a fairness constraint, with three main contributions towards the goal of quantifying the problem's inherent tradeoffs.
Cardinal welfare, individualistic ethics, and interpersonal comparisons of utility
John C. Harsanyi · 1955
Earlier work this paper cites.
On the prevention of gerrymandering
William Vickrey · 1961
Earlier work this paper cites.
A Theory of Justice
John Rawls · 1971
Earlier work this paper cites.
Uniform guidelines on employee selection procedures
EEOC · 1979
Earlier work this paper cites.
The ecological fallacy revisited: Aggregate- versus individual-level findings on economics and elections, and sociotropic voting
Gerald H. Kramer · 1983
Earlier work this paper cites.
Women, Fire, and Dangerous Things: What Categories Reveal about the Mind
George Lakoff · 1987
Earlier work this paper cites.
Game Theory and the Social Contract Volume 1: Playing Fair
Ken Bimore · 1994
Earlier work this paper cites.
Linear semi-infinite optimization , volume 2 of Wiley Series in Mathematical Methods in Practice
Miguel A. Goberna and Marco A. Lopéz · 1998
Earlier work this paper cites.
Sorting Things Out: Classification and its Consequences
Geoffrey C. Bowker and Susan Leigh Star · 1999
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The foundations of cost-sensitive learning
Charles Elkan · 2001
Earlier work this paper cites.
Natural Justice
Ken Binmore · 2005
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Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini · 2008
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Classification without discrimination
Faisal Kamiran and Toon Calders · 2009
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The Idea of Justice
Amartya K. Sen · 2009
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A discriminative model for semi-supervised learning
Maria-Florina Balcan and Avrim Blum · 2010
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Toon Calders and Sicco Verwer · 2010
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Christopher P. Chambers and Alan D. Miller · 2010
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Composite binary losses
Mark D. Reid and Robert C. Williamson · 2010
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“We are all different”: Statistical discrimination and the right to be treated as an individual
Kasper Lippert-Rasmussen · 2011
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Information, divergence and risk for binary experiments
On the statistical consistency of plug-in classifiers for non-decomposable performance measures
Harikrishna Narasimhan, Rohit Vaish, and Shivani Agarwal · 2014
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Optimizing F-measures by cost-sensitive classification
Shameem Puthiya Parambath, Nicolas Usunier, and Yves Grandvalet · 2014
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Certifying and removing disparate impact
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Optimizing non-decomposable performance measures: A tale of two classes
Harikrishna Narasimhan, Purushottam Kar, and Prateek Jain · 2015
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A survey on measuring indirect discrimination in machine learning
Indrė Žliobaitė · 2015
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Mark D Reid and Robert C Williamson · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
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Calibrated asymmetric surrogate losses
Clayton Scott · 2012
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Prediction with model-based neutrality
Kazuto Fukuchi, Jun Sakuma, and Toshihiro Kamishima · 2013
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Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
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Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Impartial predictive modeling: Ensuring fairness in arbitrary models
Kory D. Johnson, Dean P. Foster, and Robert A. Stine · 2016
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel · 2016
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Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna Gummadi · 2016
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Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
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Learning non-discriminatory predictors
Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, and Nathan Srebro · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna Gummadi · 2017
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