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Optimizing prediction accuracy can come at the expense of fairness.
Some properties of the bilevel programming problem
Jonathan F Bard · 1991
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New branch-and-bound rules for linear bilevel programming
Pierre Hansen, Brigitte Jaumard, and Gilles Savard · 1992
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Complexity issues in bilevel linear programming
Xiaotie Deng · 1998
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
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Fairness-aware learning through regularization approach
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Why unbiased computational processes can lead to discriminative decision procedures
Toon Calders and Indrė Žliobaitė · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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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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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
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Introduction to online convex optimization
Elad Hazan · 2016
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Data poisoning attacks on factorization-based collaborative filtering
Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik · 2016
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Analysis of causative attacks against svms learning from data streams
Cody Burkard and Brent Lagesse · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Learning under selective labels in the presence of expert consistency
Maria De-Arteaga, Artur Dubrawski, and Alexandra Chouldechova · 2018
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, and Massimiliano Pontil · 2018
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Decoupled Classifiers for Group-Fair and Efficient Machine Learning
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson · 2018
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
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Residual unfairness in fair machine learning from prejudiced data
Nathan Kallus and Angela Zhou · 2018
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Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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COMPAS dataset
J. Larson, S. Mattu, L. Kirchner, and J. Angwin · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
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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.
A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 2018
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Stronger data poisoning attacks break data sanitization defenses
Pang Wei Koh, Jacob Steinhardt, and Percy Liang · 2018
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Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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Recovering from biased data: Can fairness constraints improve accuracy?
Avrim Blum and Kevin Stangl · 2019
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Identifying and correcting label bias in machine learning
Heinrich Jiang and Ofir Nachum · 2019
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Noise-tolerant fair classification
Alex Lamy, Ziyuan Zhong, Aditya K Menon, and Nakul Verma · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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