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.

68

Publications

6.4K

Citations

205

Authors

92

Institutions

Publications per year

2017–2026

33

Authors

205

  1. ZS
    Zhiwei Steven Wu

    University of Pennsylvania, United States

    3 publications · 495 citations

    3

    h-index

  2. YC
    Yiling Chen

    Harvard University Press, United States

    2 publications · 147 citations

    2

    h-index

  3. OR
    Omer Reingold

    Weizmann Institute of Science, Israel

    2 publications · 3.4K citations

    2

    h-index

  4. KM
    Karima Makhlouf

    École Polytechnique, France

    2 publications · 76 citations

    2

    h-index

  5. NK
    Nisheeth K. Vishnoi

    Yale University, United States

    2 publications · 41 citations

    2

    h-index

1–5 of 205

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 566 citations

    11

    h-index

  2. Journal of Sports Economics

    JSE · 3 publications · 84 citations

    3

    h-index

  3. Nuclear Physics A

    3 publications · 95 citations

    3

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 108 citations

    2

    h-index

  5. Physical review. B, Condensed matter

    PRB · 2 publications · 83 citations

    2

    h-index

1–5