Fairness Generalization

Fairness Generalization is a theoretical framework in machine learning that characterizes the ability of a model to maintain fair decision-making performance across diverse, previously unseen domains or distributions by generalizing fairness-aware patterns beyond the training environment.

15

Publications

596

Citations

42

Authors

23

Institutions

Publications per year

2017–2026

12

Authors

42

  1. AR
    Aaron Roth

    University of Pennsylvania, United States

    3 publications · 101 citations

    3

    h-index

  2. MK
    Michael Kearns

    University of Pennsylvania, United States

    3 publications · 101 citations

    3

    h-index

  3. SJ
    Steven J. Kachelmeier

    The University of Texas at Austin, United States

    2 publications · 148 citations

    2

    h-index

  4. KL
    Kristy L. Towry

    Emory University, United States

    2 publications · 148 citations

    2

    h-index

  5. YF
    Yun Fu

    Northeastern University, United States

    2 publications · 50 citations

    2

    h-index

1–5 of 42

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 98 citations

    4

    h-index

  2. The Accounting Review

    1 publications · 126 citations

    1

    h-index

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

    Repository · 1 publications · 67 citations

    1

    h-index

  4. SSRN Electronic Journal

    Repository · 1 publications · 22 citations

    1

    h-index

  5. Machine Learning

    1 publications · 16 citations

    1

    h-index

1–5