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Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity.
Adverse impact and four-fifths rule. § 1607.4 Information on impact
e-CFR · 1981
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Security and composition of multiparty cryptographic protocols
R. Canetti · 2000
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Secure multiparty linear programming using fixed-point arithmetic
O. Catrina and S. De Hoogh · 2010
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I have a dream! (DiffeRentially privatE smArt Metering)
Gergely Ács and Claude Castelluccia · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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Decision theory for discrimination-aware classification
Faisal Kamiran, Asim Karim, and Xiangliang Zhang · 2012
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Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
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Discrimination in online ad delivery: Google ads, black names and white names, racial discrimination, and click advertising
Latanya Sweeney · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Secure Multiparty Computation and Secret Sharing
Ronald Cramer, Ivan Damgard, and Jesper Nielsen · 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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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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High-throughput semi-honest secure three-party computation with an honest majority
Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, and Kazuma Ohara · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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The movielens datasets: History and context
F. Maxwell Harper and Joseph A. Konstan · 2016
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Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
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How we analyzed the compas recidivism algorithm
Jeff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin · 2016
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Personality in computational advertising: A benchmark
Giorgio Roffo and Alessandro Vinciarelli · 2016
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Achieving differential privacy in secure multiparty data aggregation protocols on star networks
Vincent Bindschaedler, Shantanu Rane, Alejandro E. Brito, Vanishree Rao, and Ersin Uzun · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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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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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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SPD ℤ 2 k \mathbb{Z}_{2^{k}} : Efficient MPC mod 2 k 2^{k} for dishonest majority
MP-SPDZ: A versatile framework for multi-party computation
Marcel Keller · 2020
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Dana Pessach and Erez Shmueli · 2020
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Fair class balancing: enhancing model fairness without observing sensitive attributes
Shen Yan, Hsien-te Kao, and Emilio Ferrara · 2020
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Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models
Daniel Yue Zhang, Ziyi Kou, and Dong Wang · 2020
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When the curious abandon honesty: Federated learning is not private
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot · 2021
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Ronald Cramer, Ivan Damgård, Daniel Escudero, Peter Scholl, and Chaoping Xing · 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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A pragmatic introduction to secure multi-party computation
D. Evans, V. Kolesnikov, and M. Rosulek · 2018
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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Potential for discrimination in online targeted advertising
Till Speicher, Muhammad Ali, Giridhari Venkatadri, Filipe Nunes Ribeiro, George Arvanitakis, Fabrício Benevenuto, Krishna P Gummadi, Patrick Loiseau, and Alan Mislove · 2018
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Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
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Addressing algorithmic disparity and performance inconsistency in federated learning
Sen Cui, Weishen Pan, Jian Liang, Changshui Zhang, and Fei Wang · 2021
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Fantastic four: Honest-majority four-party secure computation with malicious security
Anders Dalskov, Daniel Escudero, and Marcel Keller · 2021
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Fairness-aware agnostic federated learning
Wei Du, Depeng Xu, Xintao Wu, and Hanghang Tong · 2021
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FairFed: Enabling group fairness in federated learning
Yahya H. Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and Salman Avestimehr · 2021
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Federated adversarial debiasing for fair and transferable representations
Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Zhangyang Wang, Hiroko H Dodge, and Jiayu Zhou · 2021
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Fair federated learning for heterogeneous face data
Samhita Kanaparthy, Manisha Padala, Sankarshan Damle, and Sujit Gujar · 2021
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Federated learning meets fairness and differential privacy
Manisha Padala, Sankarshan Damle, and Sujit Gujar · 2021
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Federating for learning group fair models
Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro, and Miguel Rodrigues · 2021
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Federated evaluation and tuning for on-device personalization: System design & applications
Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, et al · 2021
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Enforcing fairness in private federated learning via the modified method of differential multipliers
Borja Rodríguez-Gálvez, Filip Granqvist, Rogier van Dalen, and Matt Seigel · 2021
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LinkedIn’s job-matching AI was biased. The company’s solution? More AI
Sheridan Wall and Hilke Schellmann · 2021
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Zhe Yu · 2021
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Gifair-fl: An approach for group and individual fairness in federated learning
Xubo Yue, Maher Nouiehed, and Raed Al Kontar · 2021
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Unified group fairness on federated learning
Fengda Zhang, Kun Kuang, Yuxuan Liu, Chao Wu, Fei Wu, Jiaxun Lu, Yunfeng Shao, and Jun Xiao · 2021
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Towards fair federated learning
Zirui Zhou, Lingyang Chu, Changxin Liu, Lanjun Wang, Jian Pei, and Yong Zhang · 2021
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Google AI implements machine learning model that employs federated learning with differential privacy guarantees
Tanushree Shenwai · 2022
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