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Machine learning algorithms have been increasingly deployed in critical automated decision-making systems that directly affect human lives.
A connection between correlation and contingency
Hermann O Hirschfeld · 1935
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Das statistische problem der korrelation als variations-und eigenwertproblem und sein zusammenhang mit der ausgleichsrechnung
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Civil rights act of 1964, title vii,equal employment opportunities
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Faisal Kamiran and Toon Calders · 2009
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Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
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Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Data preprocessing techniques for classification without discrimination
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Discrimination in online ad delivery
Latanya Sweeney · 2013
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Salvatore Ruggieri · 2014
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Automated experiments on ad privacy settings
Amit Datta, Michael Carl Tschantz, and Anupam Datta · 2015
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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
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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A confidence-based approach for balancing fairness and accuracy
Benjamin Fish, Jeremy Kun, and Ádám D Lelkes · 2016
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Equality of opportunity in supervised learning
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Aditya Krishna Menon and Robert C Williamson · 2018
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Lectures on convex optimization , volume 137
Yurii Nesterov · 2018
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Gradient reversal against discrimination: A fair neural network learning approach
Edward Raff and Jared Sylvester · 2018
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Non-convex min-max optimization: Provable algorithms and applications in machine learning
Hassan Rafique, Mingrui Liu, Qihang Lin, and Tianbao Yang · 2018
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Privacy preserving clustering with constraints
Clemens Rösner and Melanie Schmidt · 2018
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A convex framework for fair regression
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Optimized pre-processing for discrimination prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 2017
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2017
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Fair kernel learning
Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-García, Jordi Muñoz-Marí, Luis Gómez-Chova, and Gustau Camps-Valls · 2017
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Learning non-discriminatory predictors
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Prasanna Sattigeri, Samuel C Hoffman, Vijil Chenthamarakshan, and Kush R Varshney · 2018
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Fair coresets and streaming algorithms for fair k-means clustering
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Fairgan: Fairness-aware generative adversarial networks
Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
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Lower bounds for finding stationary points i
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Minmax optimization: Stable limit points of gradient descent ascent are locally optimal
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Kernel methods for measuring independence
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