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Many existing fairness criteria for machine learning involve equalizing some metric across protected groups such as race or gender.
Mapping the margins: Intersectionality, identity politics, and violence against women of color
Kimberle Crenshaw · 1990
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Yearning: Race, gender, and cultural politics
Bell Hooks · 1992
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Efficient projections onto the ℓ 1 \ell_{1} -ball for learning in high dimensions
John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra · 2008
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Robust Optimization
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski · 2009
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Optimal Transport, Old and New , volume 338 of Grundlehren der Mathematischen Wissenschaften
Cédric Villani · 2009
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che hui Lien · 2009
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Tuning support vector machines for minimax and Neyman-Pearson classification
Mark A. Davenport, Richard G. Baraniuk, and Clayton D. Scott · 2010
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Theory and applications of robust optimization
Dimitris Bertsimas, David B Brown, and Constantine Caramanis · 2011
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Determinants of social desirability bias in sensitive surveys: a literature review
Ivar Krumpal · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel · 2012
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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Classification situations: Life-chances in the neoliberal era
Marion Fourcade and Kieran Healy · 2013
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Best Practices for Asking Questions to Identify Transgender and Other Gender Minority Respondents on Population-Based Surveys
The GenIUSS Group · 2014
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Satisfying real-world goals with dataset constraints
Gabriel Goh, Andrew Cotter, Maya Gupta, and Michael Friedlander · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Hongseok Namkoong and John Duchi · 2016
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Measurement Theory and Applications for the Social Sciences
Deborah L. Bandalos · 2017
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A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Scalable learning of non-decomposable objectives
Elad Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Rif A. Saurous, and Gal Elidan · 2017
Cited alongside, same era.
Counterfactual fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva · 2017
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When worlds collide: Integrating different counterfactual assumptions in fairness
Chris Russell, Matt J. Kusner, Joshua Loftus, and Ricardo Silva · 2017
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Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, and Nathan Srebro · 2017
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
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 Dudík, John Langford, and Hanna Wallach · 2018
50 years of test (un)fairness: Lessons for machine learning
Ben Hutchinson and M. Mitchell · 2019
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Abigail Z. Jacobs and Hanna Wallach · 2019
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Why equality of treatment and opportunity might matter
Niko Kolodny · 2019
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Noise-tolerant fair classification
Alexandre Lamy, Ziyuan Zhong, Aditya Krishna Menon, and Nakul Verma · 2019
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A first-order algorithmic framework for Wasserstein distributionally robust logistic regression
Jiajin Li, Sen Huang, and Anthony Man-Cho So · 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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Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, and Massimiliano Pontil · 2018
Cited alongside, same era.
Learning models with uniform performance via distributionally robust optimization
John Duchi and Hongseok Namkoong · 2018
Cited alongside, same era.
Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations
P. M. Esfahani and D. Kuhn · 2018
Cited alongside, same era.
Maya Gupta, Andrew Cotter, Mahdi Milani Fard, and Serena Wang · 2018
Cited alongside, same era.
Fairness without demographics in repeated loss minimization
Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
Cited alongside, same era.
Later among the works it cites.
Roles for computing in social change
Rediet Abebe, Solon Barocas, Jon Kleinberg, Karen Levy, Manish Raghavan, and David G Robinson · 2020
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Equalized odds postprocessing under imperfect group information
Pranjal Awasthi, Matthäus Kleindessner, and Jamie Morgenstern · 2020
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False dreams of algorithmic fairness: The case of credit pricing
Talia B Gillis · 2020
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Achieving fairness with decision trees: An adversarial approach
Vincent Grari, Boris Ruf, Sylvain Lamprier, and Marcin Detyniecki · 2020
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Towards a critical race methodology in algorithmic fairness
Alex Hanna, Emily Denton, Andrew Smart, and Jamila Smith-Loud · 2020
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Assessing algorithmic fairness with unobserved protected class using data combination
Nathan Kallus, Xiaojie Mao, and Angela Zhou · 2020
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Fairness, equality, and power in algorithmic decision-making
Maximilian Kasy and Rediet Abebe · 2020
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Fairness without demographics through adversarially reweighted learning
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H Chi · 2020
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A resolution in algorithmic fairness: Calibrated scores for fair classifications
Claire Lazar and Suhas Vijaykumar · 2020
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Fair learning with private demographic data
Hussein Mozannar, Mesrob I Ohannessian, and Nathan Srebro · 2020
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Approximate heavily-constrained learning with lagrange multiplier models
Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, and Wenshuo Guo · 2020
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Deontological ethics by monotonicity shape constraints
Serena Wang and Maya R. Gupta · 2020
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