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Seven years ago, researchers proposed a postprocessing method to equalize the error rates of a model across different demographic groups.
Manuale di economia politica: con una introduzione alla scienza sociale , volume 13
Vilfredo Pareto · 1919
Earlier work this paper cites.
An Introduction to the Bootstrap
Bradley Efron and Robert J. Tibshirani · 1994
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Improvements that don’t add up: ad-hoc retrieval results since 1998
Timothy G. Armstrong, Alistair Moffat, William Webber, and Justin Zobel · 2009
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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CVXPY: A Python-embedded modeling language for convex optimization
Steven Diamond and Stephen Boyd · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Examining additivity and weak baselines
Sadegh Kharazmi, Falk Scholer, David Vallet, and Mark Sanderson · 2016
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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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Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 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
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 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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Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2018
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The cost of fairness in binary classification
Aditya Krishna Menon and Robert C Williamson · 2018
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Propublica’s compas data revisited, 2019
Matias Barenstein · 2019
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Fairness and Machine Learning: Limitations and Opportunities
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
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Classification with fairness constraints: A meta-algorithm with provable guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, and Nisheeth K. Vishnoi · 2019
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A metric learning reality check
Kevin Musgrave, Serge Belongie, and Ser-Nam Lim · 2020
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It’s compaslicated: The messy relationship between RAI datasets and algorithmic fairness benchmarks
Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, and Suresh Venkatasubramanian · 2021
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IPUMS health surveys: Medical expenditure panel survey, version 2.1 [dataset]
Lynn A Blewett, Julia A Rivera Drew, Risa Griffin, Natalie Del Ponte, and Pat Convey · 2021
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Environment inference for invariant learning
Elliot Creager, Joern-Henrik Jacobsen, and Richard Zemel · 2021
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Promoting fairness through hyperparameter optimization
André F. Cruz, Pedro Saleiro, Catarina Belém, Carlos Soares, and Pedro Bizarro · 2021
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Retiring adult: New datasets for fair machine learning
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Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals
Andrew Cotter, Heinrich Jiang, Serena Wang, Taman Narayan, Seungil You, Karthik Sridharan, and Maya R. Gupta · 2019
Cited alongside, same era.
Are we really making much progress? a worrying analysis of recent neural recommendation approaches
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach · 2019
Cited alongside, same era.
South german credit data: Correcting a widely used data set
Ulrike Grömping · 2019
Cited alongside, same era.
50 years of test (un) fairness: Lessons for machine learning
Ben Hutchinson and Margaret Mitchell · 2019
Cited alongside, same era.
The implicit fairness criterion of unconstrained learning
Lydia T. Liu, Max Simchowitz, and Moritz Hardt · 2019
Cited alongside, same era.
Fairness constraints: A flexible approach for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P Gummadi · 2019
Cited alongside, same era.
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt · 2021
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Fairness, equality, and power in algorithmic decision-making
Maximilian Kasy and Rediet Abebe · 2021
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Fair bayesian optimization
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, and Cédric Archambeau · 2021
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Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2021
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Algorithmic fairness datasets: the story so far
Alessandro Fabris, Stefano Messina, Gianmaria Silvello, and Gian Antonio Susto · 2022
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Group-aware threshold adaptation for fair classification
Taeuk Jang, Pengyi Shi, and Xiaoqian Wang · 2022
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FairGBM: Gradient boosting with fairness constraints
André F. Cruz, Catarina Belém, Sérgio Jesus, João Bravo, Pedro Saleiro, and Pedro Bizarro · 2023
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Can fairness be automated? guidelines and opportunities for fairness-aware automl
Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, and Frank Hutter · 2023
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Fairness improves learning from noisily labeled long-tailed data, 2023
Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, and Yang Liu · 2023
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