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We tackle the problem of algorithmic fairness, where the goal is to avoid the unfairly influence of sensitive information, in the general context of regression with possible continuous sensitive attributes.
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Jonathan Borwein and Adrian S Lewis · 2010
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Data preprocessing techniques for classification without discrimination
F. Kamiran and T. Calders · 2012
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C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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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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S. Shalev-Shwartz and S. Ben-David · 2014
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M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
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Iterative orthogonal feature projection for diagnosing bias in black-box models
J. Adebayo and L. Kagal · 2016
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On fairness and calibration
G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
A. Beutel, J. Chen, Z. Zhao, and E. H. Chi · 2017
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Learning non-discriminatory predictors
B. Woodworth, S. Gunasekar, M. I. Ohannessian, and N. Srebro · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
M. B. Zafar, I. Valera, M. Gomez Rodriguez, and K. P. Gummadi · 2017
Two-stage algorithm for fairness-aware machine learning
J. Komiyama and H. Shimao · 2017
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Fair forests: Regularized tree induction to minimize model bias
E. Raff, J. Sylvester, and S. Mills · 2017
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Empirical risk minimization under fairness constraints
M. Donini, L. Oneto, S. Ben-David, J. Shawe-Taylor, and M. Pontil · 2018
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The cost of fairness in binary classification
A. K. Menon and R. C. Williamson · 2018
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Penalizing unfairness in binary classification
Y. Bechavod and K. Ligett · 2018
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From parity to preference-based notions of fairness in classification
M. B. Zafar, I. Valera, M. Rodriguez, K. Gummadi, and A. Weller · 2017
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Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. Gomez Rodriguez, and K. P. Gummadi · 2017
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, A. Roth, and Z. S. Wu · 2017
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Fair kernel learning
A. Pérez-Suay, V. Laparra, G. Mateo-García, J. Muñoz-Marí, L. Gómez-Chova, and G. Camps-Valls · 2017
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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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Optimized pre-processing for discrimination prevention
F. Calmon, D. Wei, B. Vinzamuri, K. Natesan Ramamurthy, and K. R. Varshney · 2017
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D. Alabi, N. Immorlica, and A. T. Kalai · 2018
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Decoupled classifiers for group-fair and efficient machine learning
C. Dwork, N. Immorlica, A. T. Kalai, and M. D. M. Leiserson · 2018
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Equality constrained decision trees: For the algorithmic enforcement of group fairness
J. Fitzsimons, A. Al Ali, M. Osborne, and S. Roberts · 2018
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Nonconvex optimization for regression with fairness constraints
J. Komiyama, A. Takeda, J. Honda, and H. Shimao · 2018
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Fair inference on outcomes
R. Nabi and I. Shpitser · 2018
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Learning optimal fair policies
Razieh Nabi, Daniel Malinsky, and Ilya Shpitser · 2018
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Probably approximately metric-fair learning
G. Yona and G. Rothblum · 2018
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Taking advantage of multitask learning for fair classification
L. Oneto, M. Donini, A. Elders, and M. Pontil · 2019
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