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In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamically changing environment to produce both immediate and long-term effects for individuals and demographic groups.
Some applications of extreme-value methods
Gumbel, E. J. and Lieblein, J · 1954
Earlier work this paper cites.
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Foster, D. P. and Vohra, R. V · 1992
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Coate, S. and Loury, G. C · 1993
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Rosenbaum, P. R · 2014
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