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Most work in algorithmic fairness to date has focused on discrete outcomes, such as deciding whether to grant someone a loan or not.
Calculation of amount of information about a random function contained in another such function
Gel’fand, I. M. and Yaglom, A. M · 1957
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Test bias: Prediction of grades of negro and white students in integrated colleges
Cleary, T. A · 1968
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Another look at “cultural fairness”
Darlington, R. B · 1971
Earlier work this paper cites.
A limited memory algorithm for bound constrained optimization
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C · 1995
Earlier work this paper cites.
LSAC National Longitudinal Bar Passage Study. LSAC research report series
Wightman, L. F · 1998
Earlier work this paper cites.
The elements of statistical learning , volume 1
Hastie, T., Tibshirani, R., and Friedman, J · 2001
Earlier work this paper cites.
A data-driven software tool for enabling cooperative information sharing among police departments
Redmond, M. and Baveja, A · 2002
Earlier work this paper cites.
Estimating mutual information
Kraskov, A., Stögbauer, H., and Grassberger, P · 2004
Earlier work this paper cites.
Elements of information theory
Cover, T. M. and Thomas, J. A · 2006
Earlier work this paper cites.
Random features for large-scale kernel machines
Rahimi, A. and Recht, B · 2008
Earlier work this paper cites.
Theoretical Analysis of Density Ratio Estimation
Kanamori, T., Suzuki, T., and Sugiyama, M · 2010
Cited alongside, same era.
Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting
Sugiyama, M · 2010
Cited alongside, same era.
A computationally-efficient alternative to kernel logistic regression
Sugiyama, M., Hachiya, H., Simm, J., Yamada, M., and Nam, H · 2010
Cited alongside, same era.
Fairness-Aware Classifier with Prejudice Remover Regularizer
Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J · 2012
Cited alongside, same era.
Controlling Attribute Effect in Linear Regression
Calders, T., Karim, A., Kamiran, F., Ali, W., and Zhang, X · 2013
Cited alongside, same era.
Prediction with model-based neutrality
Fukuchi, K., Kamishima, T., and Sakuma, J · 2015
Cited alongside, same era.
Estimating mutual information for discrete-continuous mixtures
Gao, W., Kannan, S., Oh, S., and Viswanath, P · 2017
Later among the works it cites.
Mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, D · 2018
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Demystifying Fixed k k -Nearest Neighbor Information Estimators
Gao, W., Oh, S., and Viswanath, P · 2018
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Fairness in supervised learning: An information theoretic approach
Ghassami, A., Khodadadian, S., and Kiyavash, N · 2018
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Nonconvex Optimization for Regression with Fairness Constraints
Komiyama, J., Takeda, A., Honda, J., and Shimao, H · 2018
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Fair regression: Quantitative definitions and reduction-based algorithms
Agarwal, A., Dudik, M., and Wu, Z. S · 2019
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Fairness Constraints: Mechanisms for Fair Classification
Zafar, M. B., Valera, I., Rodriguez, M. G., and Gummadi, K. P · 2015
Cited alongside, same era.
Machine bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
Cited alongside, same era.
A convex framework for fair regression
Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., Neel, S., and Roth, A · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
Cited alongside, same era.
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Fairness and Machine Learning
Barocas, S., Hardt, M., and Narayanan, A · 2019
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A General Framework for Fair Regression
Fitzsimons, J., Ali, A. A., Osborne, M., and Roberts, S · 2019
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50 Years of Test (Un)fairness: Lessons for Machine Learning
Hutchinson, B. and Mitchell, M · 2019
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Fairness risk measures
Williamson, R. and Menon, A · 2019
Later among the works it cites.