Fair Decision Trees

Fair Decision Trees is a decision tree learning method designed to minimize bias with respect to sensitive attributes, thereby ensuring equitable treatment across different demographic groups while maintaining predictive accuracy.

9

Publications

378

Citations

22

Authors

9

Institutions

Publications per year

2017–2026

8

Authors

22

  1. SA
    Sina Aghaei

    University of Southern California, United States

    3 publications · 158 citations

    3

    h-index

  2. PV
    Phebe Vayanos

    University of Southern California, United States

    3 publications · 158 citations

    3

    h-index

  3. WZ
    Wenbin Zhang

    University of Maryland, Baltimore County, United States

    2 publications · 101 citations

    2

    h-index

  4. MJ
    Mohammad Javad Azizi

    University of Southern California, United States

    2 publications · 137 citations

    2

    h-index

  5. EN
    Eirini Ntoutsi

    Leibniz University Hannover, Germany

    2 publications · 101 citations

    2

    h-index

1–5 of 22

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 147 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 30 citations

    2

    h-index

  3. Information Processing & Management

    1 publications · 18 citations

    1

    h-index

  4. Ecological Informatics

    1 publications · 22 citations

    1

    h-index

1–4