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The potential for learned models to amplify existing societal biases has been broadly recognized.
Sex bias in graduate admissions: Data from berkeley
P. J. Bickel, E. A. Hammel, and J. W. O’Connell · 1975
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An economic argument for affirmative action
Dean Foster and Rakesh Vohra · 1992
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Nonlinear multiobjective optimization, 1999
K. Miettinen · 1999
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The cross entropy method for classification
Shie Mannor, Dori Peleg, and Reuven Rubinstein · 2005
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Algorithms and analyses for maximal vector computation
Parke Godfrey, Ryan Shipley, and Jarek Gryz · 2007
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A coordinate gradient descent method for nonsmooth separable minimization
Paul Tseng and Sangwoon Yun · 2009
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Methods for many-objective optimization: an analysis
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Sparse group lasso and high dimensional multinomial classification
Martin Vincent and Niels Richard Hansen · 2014
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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 Kalai · 2016
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Scalable Learning of Non-Decomposable Objectives
E. E. Eban, M. Schain, A. Mackey, A. Gordon, R. A. Saurous, and G. Elidan · 2016
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The case for process fairness in learning : Feature selection for fair decision making
Nina Grgic-Hlaca · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, and Kunal Talwar · 2016
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Gender, race and the presentation of acute coronary syndrome and serious cardiopulmonary diagnoses in ed patients with chest pain
A. Allabban, JE Hollander, and JM Pines · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed Huai hsin Chi · 2017
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Multisided fairness for recommendation
Robin D. Burke · 2017
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Who makes trends? understanding demographic biases in crowdsourced recommendations
Abhijnan Chakraborty, Johnnatan Messias, Fabrício Benevenuto, Saptarshi Ghosh, Niloy Ganguly, and Krishna P. Gummadi · 2017
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UCI machine learning repository, 2017
Dua Dheeru and Efi Karra Taniskidou · 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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Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, and Kilian Q. Weinberger · 2017
Human perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction
Nina Grgic-Hlaca, Elissa M. Redmiles, Krishna P. Gummadi, and Adrian Weller · 2018
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Fairness behind a veil of ignorance: A welfare analysis for automated decision making
Hoda Heidari, Claudio Ferrari, Krishna P. Gummadi, and Andreas Krause · 2018
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A local law in relation to automated decision systems used by agencies, 2018
Corey D. Johnson Rafael Salamanca Jr. Vincent J. Gentile Robert E. Cornegy Jr. Jumaane D. Williams Ben Kallos Carlos Menchaca James Vacca, Helen K. Rosenthal · 2018
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Blind justice: Fairness with encrypted sensitive attributes
Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi, and Adrian Weller · 2018
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Does mitigating ml's impact disparity require treatment disparity?
Zachary Lipton, Julian McAuley, and Alexandra Chouldechova · 2018
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Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, and Nathan Srebro · 2017
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From parity to preference-based notions of fairness in classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi, and Adrian Weller · 2017
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Fairness Constraints: Mechanisms for Fair Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P. Gummadi · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. 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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Path-specific counterfactual fairness
Silvia Chiappa and Thomas P. S. Gillam · 2018
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Delayed impact of fair machine learning
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt · 2018
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The cost of fairness in binary classification
Aditya Krishna Menon and Robert C Williamson · 2018
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The externalities of exploration and how data diversity helps exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu · 2018
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Probably approximately metric-fair learning
Guy N. Rothblum and Gal Yona · 2018
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The price of fair pca: One extra dimension
Samira Samadi, Uthaipon Tantipongpipat, Jamie Morgenstern, Mohit Singh, and Santosh Vempala · 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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Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar · 2018
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Fairness through causal awareness: Learning causal latent-variable models for biased data
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2019
Closest in time.
Fairness and abstraction in sociotechnical systems
Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi · 2019
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