Fetching the paper…
Reading the bibliography…
Motivated by concerns that machine learning algorithms may introduce significant bias in classification models, developing fair classifiers has become an important problem in machine learning research.
Game theory, on-line prediction and boosting
Yoav Freund and Robert E Schapire · 1996
Earlier work this paper cites.
Exponentiated gradient versus gradient descent for linear predictors
Jyrki Kivinen and Manfred K Warmuth · 1997
Earlier work this paper cites.
Smooth optimization with approximate gradient
Alexandre d’Aspremont · 2008
Earlier work this paper cites.
The role of race in forecasts of violent crime
Richard Berk · 2009
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
Earlier work this paper cites.
Electronic statistics textbook
Inc. StatSoft · 2013
Earlier work this paper cites.
Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
First-order methods of smooth convex optimization with inexact oracle
Olivier Devolder, François Glineur, and Yurii Nesterov · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Earlier work this paper cites.
Unequal representation and gender stereotypes in image search results for occupations
Matthew Kay, Cynthia Matuszek, and Sean A Munson · 2015
Cited alongside, same era.
https://github.com/propublica/compas-analysis , 2016
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Cited alongside, same era.
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 W Flores, Kristin Bechtel, and Christopher T Lowenkamp · 2016
Cited alongside, same era.
Satisfying real-world goals with dataset constraints
Gabriel Goh, Andrew Cotter, Maya Gupta, and Michael P Friedlander · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
Later among the works it cites.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
Later among the works it cites.
A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
Later among the works it cites.
Classification with fairness constraints: A meta-algorithm with provable guarantees
L Elisa Celis, Lingxiao Huang, Vijay Keswani, and Nisheeth K Vishnoi · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 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.
UCI machine learning repository
Dua Dheeru and Efi Karra Taniskidou · 2017
Cited alongside, same era.
On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Recycling privileged learning and distribution matching for fairness
Novi Quadrianto and Viktoriia Sharmanska · 2017
Cited alongside, same era.
Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
Cited alongside, same era.
Michael B Cohen, Jelena Diakonikolas, and Lorenzo Orecchia · 2018
Later among the works it cites.
Adaptive sensitive reweighting to mitigate bias in fairness-aware classification
Emmanouil Krasanakis, Eleftherios Spyromitros-Xioufis, Symeon Papadopoulos, and Yiannis Kompatsiaris · 2018
Later among the works it cites.
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
Later among the works it cites.
The cost of fairness in binary classification
Aditya Krishna Menon and Robert C Williamson · 2018
Later among the works it cites.
Achieving fairness through adversarial learning: an application to recidivism prediction
Christina Wadsworth, Francesca Vera, and Chris Piech · 2018
Later among the works it cites.
Fairgan: Fairness-aware generative adversarial networks
Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu · 2018
Later among the works it cites.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
Later among the works it cites.