Multi-group Fairness

Multi-Group Fairness is a fairness framework in algorithmic decision-making that extends beyond binary group distinctions by simultaneously accounting for multiple socially salient attributes—such as race, gender, and disability status—to ensure equitable outcomes across intersecting demographic subgroups.

32

Publications

1.5K

Citations

83

Authors

42

Institutions

Publications per year

2017–2026

28

Authors

83

  1. JK
    Jon Kleinberg

    Cornell University, United States

    3 publications · 149 citations

    3

    h-index

  2. HT
    Hanghang Tong

    University of Illinois Urbana-Champaign, United States

    3 publications · 148 citations

    3

    h-index

  3. JK
    Jian Kang

    University of Illinois Urbana-Champaign, United States

    3 publications · 148 citations

    3

    h-index

  4. MB
    Muhammad Bilal Zafar

    Minhaj University Lahore, Pakistan

    2 publications · 220 citations

    2

    h-index

  5. TS
    Till Speicher

    Max Planck Institute for Software Systems, Germany

    2 publications · 220 citations

    2

    h-index

1–5 of 83

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 66 citations

    3

    h-index

  2. ACM SIGMETRICS Performance Evaluation Review

    PER · 2 publications · 23 citations

    2

    h-index

  3. Management Science

    2 publications · 42 citations

    2

    h-index

  4. Proceedings of the ACM on Human-Computer Interaction

    PACMHCI · 2 publications · 274 citations

    2

    h-index

  5. Computers & Industrial Engineering

    2 publications · 64 citations

    2

    h-index

1–5