Group Fairness

Group Fairness is a fairness criterion in algorithmic decision-making that, as a member of the class of fairness metrics, requires that outcomes or predictions be statistically comparable across predefined sensitive groups to ensure equitable treatment or impact.

353

Publications

24.9K

Citations

811

Authors

348

Institutions

Publications per year

2017–2026

241

Authors

811

  1. AR
    Aaron Roth

    University of Pennsylvania, United States

    9 publications · 1.8K citations

    9

    h-index

  2. KP
    Krishna P. Gummadi

    Max Planck Institute for Software Systems, Germany

    9 publications · 506 citations

    9

    h-index

  3. MK
    Michael Kearns

    University of Pennsylvania, United States

    9 publications · 1.8K citations

    9

    h-index

  4. HH
    Hoda Heidari

    Carnegie Mellon University, United States

    7 publications · 1.5K citations

    7

    h-index

  5. CD
    Cynthia Dwork

    IBM Research - Almaden, United States

    7 publications · 4.5K citations

    7

    h-index

1–5 of 811

Venues

  1. arXiv (Cornell University)

    Repository · 52 publications · 3K citations

    24

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 11 publications · 450 citations

    10

    h-index

  3. European Journal of Social Psychology

    EJSP · 9 publications · 409 citations

    9

    h-index

  4. SSRN Electronic Journal

    Repository · 9 publications · 244 citations

    9

    h-index

  5. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

    Repository · 7 publications · 751 citations

    7

    h-index

1–5