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A popular methodology for building binary decision-making classifiers in the presence of imperfect information is to first construct a non-binary "scoring" classifier that is calibrated over all protected groups, and then to post-process this score to obtain a binary decision.
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2011
Earlier work this paper cites.
Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks. propublica, may 23, 2016, 2016
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Earlier work this paper cites.
Amanda Bower, Sarah N Kitchen, Laura Niss, Martin J Strauss, Alexander Vargas, and Suresh Venkatasubramanian · 2017
Earlier work this paper cites.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
Cited alongside, same era.
Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
Cited alongside, same era.
Calibration for the (computationally-identifiable) masses
Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2017
Cited alongside, same era.
Paradoxes in fair computer-aided decision making
Andrew Morgan and Rafael Pass · 2017
Cited alongside, same era.
Predict responsibly: Increasing fairness by learning to defer
David Madras, Toniann Pitassi, and Richard S. Zemel · 2017
Later among the works it cites.
On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
Later among the works it cites.
Cynthia Dwork and Christina Ilvento · 2018
Closest in time.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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