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We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features.
Report to the congress on credit scoring and its effects on the availability and affordability of credit, 2007
US Federal Reserve · 2007
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Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini · 2008
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Building classifiers with independency constraints
T. Calders, F. Kamiran, and M. Pechenizkiy · 2009
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All of Statistics: A Concise Course in Statistical Inference
Larry Wasserman · 2010
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2012
Earlier work this paper cites.
Learning fair representations
Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
Cited alongside, same era.
Big data: Seizing opportunities and preserving values
John Podesta, Penny Pritzker, Ernest J. Moniz, John Holdren, and Jefrey Zients · 2014
Cited alongside, same era.
A multidisciplinary survey on discrimination analysis
Andrea Romei and Salvatore Ruggieri · 2014
Cited alongside, same era.
Learning fair classifiers
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2015
Cited alongside, same era.
Certifying and removing disparate impact
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Cited alongside, same era.
The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel · 2015
Later among the works it cites.
On the relation between accuracy and fairness in binary classification
Indre Zliobaite · 2015
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Big data’s disparate impact
Solon Barocas and Andrew Selbst · 2016
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Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
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Executive Office of the President
Big data: A report on algorithmic systems, opportunity, and civil rights · 2016
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