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With the growing awareness to fairness in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious interest in notions of fair data representations.
k-anonymity: A model for protecting privacy
Latanya Sweeney · 2002
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.
Does “ban the box” help or hurt low-skilled workers? statistical discrimination and employment outcomes when criminal histories are hidden
Jennifer L Doleac and Benjamin Hansen · 2016
Earlier work this paper cites.
Censoring representations with an adversary
Harrison Edwards and Amos J. Storkey · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
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.
Optimized pre-processing for discrimination prevention
Flávio du Pin Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney · 2017
Cited alongside, same era.
Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning
Nina Grgic-Hlaca, Muhammad Bilal Zafar, Krishna P. Gummadi, and Adrian Weller · 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.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
Later among the works it cites.
Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel · 2019
Later among the works it cites.
Costs and benefits of fair representation learning
Daniel McNamara, Cheng Soon Ong, and Robert C Williamson · 2019
Later among the works it cites.
Learning fair and transferable representations
Luca Oneto, Michele Donini, Andreas Maurer, and Massimiliano Pontil · 2019
Later among the works it cites.
Learning controllable fair representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, and Stefano Ermon · 2019
Later among the works it cites.
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