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Algorithmic fairness involves expressing notions such as equity, or reasonable treatment, as quantifiable measures that a machine learning algorithm can optimise.
R. Williamson and A. Menon · 1901
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Calculation of amount of information about a random function contained in another such function
I. M. Gel’fand and A. M. Yaglom · 1957
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Test bias: Prediction of grades of negro and white students in integrated colleges
T. A. Cleary · 1968
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Another look at “cultural fairness”
R. B. Darlington · 1971
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Inferences for Case-Control and Semiparametric Two-Sample Density Ratio Models
J. Qin · 1998
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The elements of statistical learning , volume 1
T. Hastie, R. Tibshirani, and J. Friedman · 2001
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Elements of information theory
T. M. Cover and J. A. Thomas · 2006
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Discriminative learning under covariate shift
S. Bickel, M. Brückner, and T. Scheffer · 2009
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A Least-squares Approach to Direct Importance Estimation
T. Kanamori, S. Hido, and M. Sugiyama · 2009
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Density Ratio Estimation: A Comprehensive Review
M. Sugiyama, T. Suzuki, and T. Kanamori · 2010
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Fairness-Aware Classifier with Prejudice Remover Regularizer
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2012
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Machine Learning with Squared-Loss Mutual Information
M. Sugiyama · 2012
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Density ratio estimation in machine learning
M. Sugiyama, T. Suzuki, and T. Kanamori · 2012
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Controlling Attribute Effect in Linear Regression
T. Calders, A. Karim, F. Kamiran, W. Ali, and X. Zhang · 2013
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Prediction with model-based neutrality
K. Fukuchi, T. Kamishima, and J. Sakuma · 2015
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Estimating mutual information in high dimensions via classification error
C. Y. Zheng and Y. Benjamini · 2016
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A convex framework for fair regression
R. Berk, H. Heidari, S. Jabbari, M. Joseph, M. Kearns, J. Morgenstern, S. Neel, and A. Roth · 2017
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UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
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Fairness in supervised learning: An information theoretic approach
A. Ghassami, S. Khodadadian, and N. Kiyavash · 2018
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Fairness and Machine Learning
S. Barocas, M. Hardt, and A. Narayanan · 2019
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M. B. Zafar, I. Valera, M. G. Rodriguez, and K. P. Gummadi · 2015
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Machine bias
J. Angwin, J. Larson, S. Mattu, and L. Kirchner · 2016
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J. Fitzsimons, A. A. Ali, M. Osborne, and S. Roberts · 2019
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50 Years of Test (Un)fairness: Lessons for Machine Learning
B. Hutchinson and M. Mitchell · 2019
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Costs and Benefits of Fair Representation Learning
D. McNamara, C. S. Ong, and R. C. Williamson · 2019
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