Calibration Fairness

Calibration Fairness is a fairness metric in machine learning that requires a predictive model to produce calibrated probabilities—where the predicted probability of an outcome matches the true likelihood of that outcome—across all defined demographic groups, thereby ensuring group-level equivalence in the reliability of predicted scores.

8

Publications

490

Citations

27

Authors

13

Institutions

Publications per year

2017–2026

8

Authors

27

  1. SE
    Stefano Ermon

    Stanford University, United States

    2 publications · 25 citations

    2

    h-index

  2. TM
    Tengyu Ma

    Stanford University, United States

    2 publications · 25 citations

    2

    h-index

  3. SZ
    Shengjia Zhao

    Stanford University, United States

    2 publications · 25 citations

    2

    h-index

  4. WH
    Wen Huan

    University of Arkansas at Fayetteville, United States

    1 publications · 22 citations

    1

    h-index

  5. GP
    Geoff Pleiss

    Cornell University, United States

    1 publications · 290 citations

    1

    h-index

1–5 of 27

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 364 citations

    4

    h-index

  2. Journal of Moral Philosophy

    1 publications · 42 citations

    1

    h-index

  3. Companion Proceedings of the Web Conference 2020

    Conference · 1 publications · 22 citations

    1

    h-index

  4. Journal of Biomedical Informatics

    1 publications · 11 citations

1–4