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In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race.
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Fairness-aware classifier with prejudice remover regularizer
Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J · 2012
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The federal post conviction risk assessment (pcra): A construction and validation study
Lowenkamp, C., L Johnson, J., Holsinger, A., Vanbenschoten, S., and R Robinson, C · 2012
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Calders, T., Karim, A., Kamiran, F., Ali, W., and Zhang, X · 2013
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Fukuchi, K., Sakuma, J., and Kamishima, T · 2013
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Lichman, M · 2013
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Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., and Raghavan, M · 2017
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Fair kernel learning
Pérez-Suay, A., Laparra, V., Mateo-García, G., Muñoz-Marí, J., Gómez-Chova, L., and Camps-Valls, G · 2017
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Alabi, D., Immorlica, N., and Kalai, A · 2018
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Buolamwini, J. and Gebru, T · 2018
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Hardt, M., Price, E., and Srebro, N · 2016
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Why is my classifier discriminatory?
Chen, I., Johansson, F. D., and Sontag, D · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
Corbett-Davies, S. and Goel, S · 2018
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Gurobi optimizer reference manual, 2018
Gurobi Optimization, L · 2018
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Nonconvex optimization for regression with fairness constraints
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Fairness and abstraction in sociotechnical systems
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Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making
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