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Systemic bias with respect to gender, race and ethnicity, often unconscious, is prevalent in datasets involving choices among individuals.
An outer-approximation algorithm for a class of mixed-integer nonlinear programs
M. A. Duran and I. E. Grossmann · 1986
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Solving mixed integer nonlinear programs by outer approximation
R. Fletcher and S. Leyffer · 1994
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Lsac national longitudinal bar passage study. lsac research report series
L. F. Wightman · 1998
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Discrimination-aware data mining
D. Pedreshi, S. Ruggieri, and F. Turini · 2008
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Building classifiers with independency constraints
T. Calders, F. Kamiran, and M. Pechenizkiy · 2009
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I.-C. Yeh and C.-h. Lien · 2009
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Handling conditional discrimination
I. Zliobaite, F. Kamiran, and T. Calders · 2011
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When do the ends justify the means? evaluating procedural fairness
D. Doherty and J. Wolak · 2012
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Data preprocessing techniques for classification without discrimination
F. Kamiran and T. Calders · 2012
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Censoring representations with an adversary
H. Edwards and A. Storkey · 2015
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Computational fairness: Preventing machine-learned discrimination.(2015)
M. Feldman · 2015
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Certifying and removing disparate impact
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
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Machine bias
J. Angwin, J. Larson, S. Mattu, and L. Kirchner · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
T. Bolukbasi, K.-W. Chang, J. Zou, V. Saligrama, and A. Kalai · 2016
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Satisfying real-world goals with dataset constraints
G. Goh, A. Cotter, M. Gupta, and M. Friedlander · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
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Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. G. Rogriguez, and K. P. Gummadi · 2017
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A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach · 2018
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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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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, A. Roth, and Z. S. Wu · 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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Learning with complex loss functions and constraints
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J. Kleinberg, S. Mullainathan, and M. Raghavan · 2016
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Optimal classification trees
D. Bertsimas and J. Dunn · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
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Algorithmic decision making and the cost of fairness
S. Corbett-Davies, E. Pierson, A. Feller, S. Goel, and A. Huq · 2017
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G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger · 2017
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Learning non-discriminatory predictors
B. Woodworth, S. Gunasekar, M. I. Ohannessian, and N. Srebro · 2017
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H. Narasimhan · 2018
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Machine Learning under a Modern Optimization Lens
D. Bertsimas and J. Dunn · 2019
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Robust classification
D. Bertsimas, J. Dunn, C. Pawlowski, and Y. D. Zhuo · 2019
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Fairness in recommendation ranking through pairwise comparisons
A. Beutel, J. Chen, T. Doshi, H. Qian, L. Wei, Y. Wu, L. Heldt, Z. Zhao, L. Hong, E. H. Chi, et al · 2019
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Training well-generalizing classifiers for fairness metrics and other data-dependent constraints
A. Cotter, M. Gupta, H. Jiang, N. Srebro, K. Sridharan, S. Wang, B. Woodworth, and S. You · 2019
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Identifying key predictors of recidivism among offenders attending a batterer intervention program: A survival analysis
M. Lila, M. Martín Fernández, E. Gracia Fuster, J. J. López Ossorio, and J. L. González · 2019
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