Intersectional Fairness

Intersectional Fairness is a fairness evaluation framework that extends beyond individual protected attributes by systematically accounting for the compounded disadvantages arising from the overlapping identities of marginalized groups in algorithmic systems.

17

Publications

714

Citations

48

Authors

27

Institutions

Publications per year

2017–2026

17

Authors

48

  1. SP
    Shimei Pan

    University of Maryland, Baltimore County, United States

    3 publications · 237 citations

    3

    h-index

  2. JR
    James R. Foulds

    University of Maryland, Baltimore County, United States

    3 publications · 237 citations

    3

    h-index

  3. KN
    Kamrun Naher Keya

    University of Maryland, Baltimore County, United States

    2 publications · 217 citations

    2

    h-index

  4. RI
    Rashidul Islam

    University of Maryland, Baltimore County, United States

    2 publications · 217 citations

    2

    h-index

  5. MR
    Mary Reagan

    Washington University in St. Louis, United States

    1 publications · 10 citations

    1

    h-index

1–5 of 48

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 79 citations

    5

    h-index

  2. 2022 ACM Conference on Fairness, Accountability, and Transparency

    Conference · 2 publications · 139 citations

    2

    h-index

  3. Big Data & Society

    1 publications · 130 citations

    1

    h-index

  4. 2022 IEEE 38th International Conference on Data Engineering (ICDE)

    Conference · 1 publications · 14 citations

    1

    h-index

  5. 2022 IEEE International Conference on Big Data (Big Data)

    Conference · 1 publications · 18 citations

    1

    h-index

1–5