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We study the problem of fair classification within the versatile framework of Dwork et al.
How to construct random functions
Oded Goldreich, Shafi Goldwasser, and Silvio Micali · 1984
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A theory of the learnable
Leslie G. Valiant · 1984
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Toward efficient agnostic learning
Michael J. Kearns, Robert E. Schapire, and Linda M. Sellie · 1994
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Efficient noise-tolerant learning from statistical queries
Michael Kearns · 1998
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Agnostically learning decision trees
Parikshit Gopalan, Adam Tauman Kalai, and Adam R Klivans · 2008
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Primal-dual subgradient methods for convex problems
Yurii Nesterov · 2009
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Distribution-specific agnostic boosting
Vitaly Feldman · 2010
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2012
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Agnostic learning of monomials by halfspaces is hard
Vitaly Feldman, Venkatesan Guruswami, Prasad Raghavendra, and Yi Wu · 2012
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 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
Avi Feller, Emma Pierson, Sam Corbett-Davies, and Sharad Goel · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Cited alongside, same era.
How algorithms can bring down minorities’ credit scores
Kaveh Waddell · 2016
Cited alongside, same era.
Pseudorandom functions: Three decades later
Andrej Bogdanov and Alon Rosen · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger · 2017
Later among the works it cites.
Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Closest in time.
Online learning with an unknown fairness metric
Stephen Gillen, Christopher Jung, Michael J. Kearns, and Aaron Roth · 2018
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Calibration for the (computationally-identifiable) masses
Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
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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.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Cited alongside, same era.
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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Multiaccuracy: Black-box post-processing for fairness in classification
Michael P. Kim, Amirata Ghorbani, and James Zou · 2018
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Probably approximately metric-fair learning
Guy N. Rothblum and Gal Yona · 2018
Closest in time.