Equal Opportunity and Affirmative Action via Counterfactual Predictions

Repairing without retraining: Avoiding disparate impact with counterfactual distributions

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Integration, affirmative action, and strict scrutiny

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Causality

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Causality

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Fairness through awareness

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Data preprocessing techniques for classification without discrimination

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Fairness through awareness

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Learning fair representations

Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013) · 2013

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Grade: Machine learning support for graduate admissions

Waters, A. & Miikkulainen, R. (2014) · 2014

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Big data’s disparate impact

Barocas, S. & Selbst, A. D. (2016) · 2016

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Equality of opportunity in supervised learning

Hardt, M., Price, E., Srebro, N., et al. (2016) · 2016

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Inherent trade-offs in the fair determination of risk scores

Original

Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016) · 2016

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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

Chouldechova, A. (2017) · 2017

Elements of causal inference: foundations and learning algorithms

Peters, J., Janzing, D., & Schölkopf, B. (2017) · 2017

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Fairness constraints: Mechanisms for fair classification

Zafar, M. B., Valera, I., Rodriguez, M. G., & Gummadi, K. P. (2017) · 2017

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Counterfactual fairness

Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017) · 2017

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Fairness without demographics in repeated loss minimization

Hashimoto, T. B., Srivastava, M., Namkoong, H., & Liang, P. (2018) · 2018

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Residual unfairness in fair machine learning from prejudiced data

Kallus, N. & Zhou, A. (2018) · 2018

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Fairness in decision-making–the causal explanation formula

Zhang, J. & Bareinboim, E. (2018) · 2018

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