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We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances.
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M. Feldman · 2015
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M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
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Fair boosting: a case study
B. Fish, J. Kun, and A. D. Lelkes · 2015
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M. Malekipirbazari and V. Aksakalli · 2015
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J. Weed and F. Bach · 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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A reduction approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach · 2018
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M. Hardt, E. Price, and N. Srebro · 2016
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The variational fair autoencoder
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel · 2016
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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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Optimized pre-processing for discrimination prevention
F. Calmon, D. Wei, B. Vinzamuri, K. N. Ramamurthy, and K. R. Varshney · 2017
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Generative modeling using the sliced Wasserstein distance
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Empirical risk minimization under fairness constraints
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Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
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Nonconvex optimization for regression with fairness constraints
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Learning with complex loss functions and constraints
H. Narasimhan · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
S. Wu, M. Kearns, S. Neel, and A. Roth · 2018
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Mitigating unwanted biases with adversarial learning
B. Zhang, H Lemoine, and M. Mitchell · 2018
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Path-specific counterfactual fairness
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An algorithm for removing sensitive information: Application to race-independent recidivism prediction
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