Fetching the paper…
Reading the bibliography…
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier across these groups.
Some notes on computation of games solutions , Jan 1949
George W. Brown · 1949
Earlier work this paper cites.
An iterative method of solving a game
Julia Robinson · 1951
Earlier work this paper cites.
On general minimax theorems
Maurice Sion · 1958
Earlier work this paper cites.
An Introduction to Computational Learning Theory
Michael J Kearns and Umesh Virkumar Vazirani · 1994
Earlier work this paper cites.
Toward efficient agnostic learning
Michael J Kearns, Robert E Schapire, and Linda M Sellie · 1994
Earlier work this paper cites.
Game theory, on-line prediction and boosting
Yoav Freund and Robert E. Schapire · 1996
Earlier work this paper cites.
Cost-sensitive learning by cost-proportionate example weighting
Bianca Zadrozny, John Langford, and Naoki Abe · 2003
Earlier work this paper cites.
Efficient algorithms for online decision problems
Adam Tauman Kalai and Santosh Vempala · 2005
Earlier work this paper cites.
On agnostic boosting and parity learning
Adam Tauman Kalai, Yishay Mansour, and Elad Verbin · 2008
Earlier work this paper cites.
Hardness results for agnostically learning low-degree polynomial threshold functions
Ilias Diakonikolas, Ryan O’Donnell, Rocco A. Servedio, and Yi Wu · 2011
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Cited alongside, same era.
Agnostic learning of monomials by halfspaces is hard
Vitaly Feldman, Venkatesan Guruswami, Prasad Raghavendra, and Yi Wu · 2012
Cited alongside, same era.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
Cited alongside, same era.
A methodology for direct and indirect discrimination prevention in data mining
Sara Hajian and Josep Domingo-Ferrer · 2013
Cited alongside, same era.
A counter-example to Karlin’s strong conjecture for fictitious play
Constantinos Daskalakis and Qinxuan Pan · 2014
Cited alongside, same era.
The new science of sentencing
Anna Maria Barry-Jester, Ben Casselman, and Dana Goldstein · 2016
Cited alongside, same era.
Predictive policing using machine learning to detect patterns of crime
Cynthia Rudin · 2016
Later among the works it cites.
Identifying significant predictive bias in classifiers
Zhe Zhang and Daniel B Neill · 2016
Later among the works it cites.
A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, and John Langford · 2017
Closest in time.
Facebook’s secret censorship rules protect white men from hate speech but not black children
Julia Angwin and Hannes Grassegger · 2017
Closest in time.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On the (im) possibility of fairness
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Cited alongside, same era.
Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 2016
Cited alongside, same era.
What does that web search say about your credit?
James Rufus Koren · 2016
Cited alongside, same era.
Úrsula Hébert-Johnson, Michael P Kim, Omer Reingold, and Guy N Rothblum · 2017
Closest in time.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Closest in time.
Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
Closest in time.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
Closest in time.
An Empirical Study of Rich Subgroup Fairness for Machine Learning
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Closest in time.