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The use of algorithmic decision making systems in domains which impact the financial, social, and political well-being of people has created a demand for these decision making systems to be "fair" under some accepted notion of equity.
Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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Adversarial machine learning
Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and J Doug Tygar · 2011
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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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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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Hiring by algorithm: Predicting and preventing disparate impact, 2016
Ifeoma Ajunwa, Sorelle A. Friedler, C. Scheidegger, and S. Venkatasubramanian · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
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Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning, 2018
Sam Corbett-Davies and Sharad Goel · 2018
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Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, and Zhiwei Steven Wu · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Adversarial machine learning
Yevgeniy Vorobeychik and Murat Kantarcioglu · 2018
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Lily Hu, Nicole Immorlica, and Jennifer Wortman Vaughan · 2019
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The social cost of strategic classification
Smitha Milli, John Miller, Anca D. Dragan, and Moritz Hardt · 2019
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Fairness constraints: A flexible approach for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi · 2019
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Manipulation-proof machine learning
Daniel Björkegren, Joshua E. Blumenstock, and Samsun Knight · 2020
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Learning strategy-aware linear classifiers
Yiling Chen, Yang Liu, and Chara Podimata · 2020
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Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Strategic classification
Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro
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To be robust or to be fair: towards fairness in adversarial training
Han Xu, Xiaorui Liu, Yaxin Li, Anil K Jain, and Jiliang Tang · 2021
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