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We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems.
Scaling up the accuracy of naive Bayes classifiers: A decision-tree hybrid
Ron Kohavi · 1996
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A data-driven software tool for enabling cooperative information sharing among police departments
Michael Redmond and Alok Baveja · 2002
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Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini · 2008
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che-hui Lien · 2009
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Three naive Bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
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Discrimination aware decision tree learning
Faisal Kamiran, Toon Calders, and Mykola Pechenizkiy · 2010
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2011
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k-NN as an implementation of situation testing for discrimination discovery and prevention
Binh Thanh Luong, Salvatore Ruggieri, and Franco Turini · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Decision theory for discrimination-aware classification
Faisal Kamiran, Asim Karim, and Xiangliang Zhang · 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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Controlling attribute effect in linear regression
Toon Calders, Asim Karim, Faisal Kamiran, Wasif Ali, and Xiangliang Zhang · 2013
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A methodology for direct and indirect discrimination prevention in data mining
Sara Hajian and Josep Domingo-Ferrer · 2013
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UCI machine learning repository, 2013
Moshe Lichman · 2013
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Discrimination in online ad delivery
Latanya Sweeney · 2013
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Learning fair representations
Richard Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Certifying and removing disparate impact
Michael Feldman, Sorelle Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Towards diagnosing accuracy loss in discrimination-aware classification: An application to predictive policing
Zubin Jelveh and Michael Luca · 2015
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On the (im)possibility of fairness
Sorelle Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Impartial predictive modeling: Ensuring fairness in arbitrary models
Kory Johnson, Dean Foster, and Robert Stine · 2016
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Fairness in learning: classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth · 2016
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Can an algorithm hire better than a human?
Clair Miller · 2016
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Predictive policing using machine learning to detect patterns of crime
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Auditing black-box models for indirect influence
Philip Adler, Casey Falk, Sorelle Friedler, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian · 2016
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Machine bias
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The new science of sentencing
Anna Barry-Jester, Ben Casselman, and Dana Goldstein · 2016
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Artificial intolerance
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A confidence-based approach for balancing fairness and accuracy
Benjamin Fish, Jeremy Kun, and Ádám Dániel Lelkes · 2016
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Cynthia Rudin · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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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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Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
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Learning non-discriminatory predictors
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
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