Statistical Parity
Statistical Parity is a fairness metric in machine learning that belongs to the class of statistical group fairness criteria and is defined by the condition that individuals from different protected and unprotected groups have equal probabilities of being assigned to the positive outcome class.
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6.4K
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205
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92
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2017–2026
33
68
Cynthia Dwork, Moritz Hardt, Toniann Pitassi et al. · 2012 · 3.4K citations
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Michael Kearns, Seth Neel, Aaron Roth et al. · arXiv (Cornell University) · 2017 · 306 citations · Full text
Female hunters of the early Americas
Randall Haas, James T. Watson, Tammy Buonasera et al. · Science Advances · 2020 · 157 citations · Full text
A Short-term Intervention for Long-term Fairness in the Labor Market
Lily Hu, Yiling Chen · 2018 · 132 citations · Full text
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92

Centre National de la Recherche Scientifique
Paris, France
5 h-index
28 publications
714 citations
Université Libre de Bruxelles
Brussels, Belgium
3 h-index
5 publications
203 citations
University of Zurich
Zurich, Switzerland
3 h-index
4 publications
143 citations

University of Toronto
Toronto, Canada
2 h-index
4 publications
6.8K citations
ZHAW Zurich University of Applied Sciences
Winterthur, Switzerland
2 h-index
3 publications
114 citations

University of Arkansas at Fayetteville
Fayetteville, United States
2 h-index
4 publications
153 citations
University of California, Irvine
Irvine, United States
2 h-index
3 publications
177 citations
Microsoft (United States)
Redmond, United States
2 h-index
4 publications
6.8K citations
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Repository · 11 publications · 566 citations
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JSE · 3 publications · 84 citations
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3 publications · 95 citations
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Conference · 2 publications · 108 citations
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PRB · 2 publications · 83 citations
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