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As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences.
Putting fairness principles into practice: Challenges, metrics, and improvements
A. Beutel, J. Chen, T. Doshi, H. Qian, A. Woodruff, C. Luu, P. Kreitmann, J. Bischof, and E. H. Chi · 1901
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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, and C. Goodrow · 1903
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Nuanced metrics for measuring unintended bias with real data for text classification
D. Borkan, L. Dixon, J. Sorensen, N. Thain, and L. Vasserman · 1903
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Transfer of machine learning fairness across domains
C. Schumann, X. Wang, A. Beutel, J. Chen, H. Qian, and E. H. Chi · 1906
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A kernel method for the two-sample problem
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. J. Smola · 2008
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Three naive bayes approaches for discrimination-free classification
T. Calders and S. Verwer · 2010
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
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Fairness constraints: A mechanism for fair classification
M. Zafar, I. Valera, M. Rodriguez, and K. P. Gummadi · 2015
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K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
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Satisfying real-world goals with dataset constraints
G. Goh, A. Cotter, M. Gupta, and M. P. Friedlander · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
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Data decisions and theoretical implications when adversarially learning fair representations
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, A. Roth, and Z. S. Wu · 2017
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A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. M. Wallach · 2018
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Measuring and mitigating unintended bias in text classification
L. Dixon, J. Li, J. Sorensen, N. Thain, and L. Vasserman · 2018
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Does mitigating ml’s impact disparity require treatment disparity?
Z. Lipton, J. McAuley, and A. Chouldechova · 2018
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Learning adversarially fair and transferable representations
D. Madras, E. Creager, T. Pitassi, and R. S. Zemel · 2018
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A. Beutel, J. Chen, Z. Zhao, and E. H. Chi · 2017
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UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
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Mitigating unwanted biases with adversarial learning
B. H. Zhang, B. Lemoine, and M. Mitchell · 2018
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The fairness of risk scores beyond classification: Bipartite ranking and the xauc metric
N. Kallus and A. Zhou · 2019
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