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Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups.
Race bias, social class bias, and gender bias in clinical judgment
Howard N. Garb · 1997
Earlier work this paper cites.
Asymptotic calibration
Dean P. Foster and Rakesh V. Vohra · 1998
Earlier work this paper cites.
Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
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Discrimination and racial disparities in health: Evidence and needed research
David R. Williams and Selina A. Mohammed · 2009
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Three naive bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
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Fairness-aware learning through regularization approach
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2012
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A methodology for direct and indirect discrimination prevention in data mining
Sara Hajian and Josep Domingo-Ferrer · 2013
Earlier work this paper cites.
Discrimination in online ad delivery
Latanya Sweeney · 2013
Earlier work this paper cites.
Learning Fair Representations
Richard S Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
A multidisciplinary survey on discrimination analysis
Andrea Romei and Salvatore Ruggieri · 2014
Cited alongside, same era.
Automated experiments on ad privacy settings
Amit Datta, Michael Carl Tschantz, and Anupam Datta · 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.
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna Gummadi · 2015
Cited alongside, same era.
A survey on measuring indirect discrimination in machine learning
Indre Zliobaite · 2015
Cited alongside, same era.
Assessing calibration of prognostic risk scores
Cynthia S. Crowson, Elizabeth J. Atkinson, and Terry M. Therneau · 2016
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COMPAS risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
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False positives, false negatives, and false analyses: A rejoinder to “machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks.”
Anthony Flores, Christopher Lowenkamp, and Kristin Bechtel · 2016
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On the (im) possibility of fairness
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
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Ethics for powerful algorithms (1 of 4)
Abe Gong · 2016
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Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Cited alongside, same era.
Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2016
Cited alongside, same era.
A computer program used for bail and sentencing decisions was labeled biased against blacks. it’s actually not that clear
Sam Corbett-Davies, Emma Pierson, Avi Feller, and Sharad Goel · 2016
Cited alongside, same era.
https://docs.google.com/document/d/1pKtyl8XmJH7Z09lxkb70n6fa2Fiitd7ydbxgCT_wCXs/edit?pref=2&pli=1
Propublica analysis
Cited in the paper.
Fair algorithms and the equal treatment principle
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq
Cited in the paper.
Moritz Hardt, Eric Price, and Nathan Srebro · 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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How we analyzed the COMPAS recidivism algorithm
Jeff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin · 2016
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Big data: A report on algorithmic systems, opportunity, and civil rights
Executive Office of the President · 2016
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