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Group fairness, a class of fairness notions that measure how different groups of individuals are treated differently according to their protected attributes, has been shown to conflict with one another, often with a necessary cost in loss of model's predictive performance.
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Adverse impact and test validation: A practitioner’s guide to valid and defensible employment testing
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Linear programming and extensions
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Discrimination aware decision tree learning
Kamiran, F., Calders, T., and Pechenizkiy, M · 2010
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Fairness through awareness
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Learning fair representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C · 2013
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Certifying and removing disparate impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S · 2015
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On the relation between accuracy and fairness in binary classification
Žliobaitė, I · 2015
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Fairness constraints: Mechanisms for fair classification
Zafar, M. B., Valera, I., Rodriguez, M. G., and Gummadi, K. P · 2015
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A · 2017
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UCI machine learning repository
Dua, D. and Graff, C · 2017
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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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Learning adversarially fair and transferable representations
Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
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The cost of fairness in binary classification
Menon, A. K. and Williamson, R. C · 2018
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Translation tutorial: 21 fairness definitions and their politics
Narayanan, A · 2018
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The price of fair PCA: One extra dimension
Samadi, S., Tantipongpipat, U., Morgenstern, J. H., Singh, M., and Vempala, S · 2018
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Fairness definitions explained
Verma, S. and Rubin, J · 2018
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Classification with fairness constraints: A meta-algorithm with provable guarantees
Celis, L. E., Huang, L., Keswani, V., and Vishnoi, N. K · 2019
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On fairness and calibration
Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., and Weinberger, K. Q · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Zafar, M. B., Valera, I., Gomez Rodriguez, M., and Gummadi, K. P · 2017
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Bellamy, R. K., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al · 2018
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Fairness in criminal justice risk assessments: The state of the art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., and Roth, A · 2018
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A statistical approach to adult census income level prediction
Chakrabarty, N. and Biswas, S · 2018
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Ai now 2019 report, 2019
Crawford, K., Dobbe, R., Dryer, T., Fried, G., Green, B., Kaziunas, E., Kak, A., Mathur, V., McElroy, E., Sánchez, A. N., Raji, D., Rankin, J. L., Richardson, R., Schultz, J., West, S. M., and Whittaker, M · 2019
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The implicit fairness criterion of unconstrained learning
Liu, L. T., Simchowitz, M., and Hardt, M · 2019
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Learning controllable fair representations
Song, J., Kalluri, P., Grover, A., Zhao, S., and Ermon, S · 2019
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Inherent tradeoffs in learning fair representations
Zhao, H. and Gordon, G. J · 2019
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Learning fair representations for kernel models
Tan, Z., Yeom, S., Fredrikson, M., and Talwalkar, A · 2020
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