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It is well understood that a system built from individually fair components may not itself be individually fair.
Clustering social networks
Nina Mishra, Robert Schreiber, Isabelle Stanton, and Robert E. Tarjan · 2007
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2012
Earlier work this paper cites.
Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
Automated experiments on ad privacy settings
Amit Datta, Michael Carl Tschantz, and Anupam Datta · 2015
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Earlier work this paper cites.
To predict and serve?
Kristian Lum and William Isaac · 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
Earlier work this paper cites.
Fairness at equilibrium in the labor market
Lily Hu and Yiling Chen · 2017
Earlier work this paper cites.
Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Cited alongside, same era.
On conditional parity as a notion of non-discrimination in machine learning
Ya’acov Ritov, Yuekai Sun, and Ruofei Zhao · 2017
Cited alongside, same era.
Runaway feedback loops in predictive policing
Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger, and Suresh Venkatasubramanian · 2018
Cited alongside, same era.
Online learning with an unknown fairness metric
Stephen Gillen, Christopher Jung, Michael J. Kearns, and Aaron Roth · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Probably approximately metric-fair learning
Gal Yona and Guy N. Rothblum · 2018
Later among the works it cites.
Toward controlling discrimination in online ad auctions
L. Elisa Celis, Anay Mehrotra, and Nisheeth K. Vishnoi · 2019
Later among the works it cites.
Fairness under composition
Cynthia Dwork and Christina Ilvento · 2019
Later among the works it cites.
Learning from outcomes: Evidence-consistent rankings
Cynthia Dwork, Michael Kim, Omer Reingold, Guy Rothblum, and Gal Yona · 2019
Later among the works it cites.
Eliciting and enforcing subjective individual fairness
Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, and Zhiwei Steven Wu · 2019
Later among the works it cites.
Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads
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Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael J. Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Cited alongside, same era.
Fairness through computationally-bounded awareness
Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
Cited alongside, same era.
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard S. Zemel · 2018
Cited alongside, same era.
Anja Lambrecht and Catherine Tucker · 2019
Later among the works it cites.
Delayed impact of fair machine learning
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt · 2019
Later among the works it cites.
Multi-category fairness in sponsored search auctions
Christina Ilvento, Meena Jagadeesan, and Shuchi Chawla · 2020
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