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We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting.
On the difficulty of approximately maximizing agreements
Shai Ben-David, Nadav Eiron, and Philip M Long · 2003
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Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
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Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
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Three naive bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
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Fairness-aware learning through regularization approach
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2015
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Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2015
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Compas risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Cited alongside, same era.
Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2017
Cited alongside, same era.
Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
Cited alongside, same era.
The cost of fairness in binary classification
Aditya Krishna Menon and Robert C Williamson · 2018
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Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
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Translation tutorial: 21 fairness definitions and their politics
Arvind Narayanan · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
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One-network adversarial fairness
Tameem Adel, Isabel Valera, Zoubin Ghahramani, and Adrian Weller · 2019
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Counterfactual risk assessments, evaluation, and fairness
Amanda Coston, Alexandra Chouldechova, and Edward H Kennedy · 2019
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Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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
Cited alongside, same era.
The frontiers of fairness in machine learning
Alexandra Chouldechova and Aaron Roth · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
Cited alongside, same era.
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
Cited alongside, same era.
Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel · 2019
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Wasserstein fair classification
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, and Silvia Chiappa · 2019
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An algorithm for removing sensitive information: application to race-independent recidivism prediction
James E Johndrow, Kristian Lum, et al · 2019
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Fairness through causal awareness: Learning causal latent-variable models for biased data
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2019
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Equal opportunity and affirmative action via counterfactual predictions
Yixin Wang, Dhanya Sridhar, and David M Blei · 2019
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Inherent tradeoffs in learning fair representations
Han Zhao and Geoffrey J Gordon · 2019
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