Predictive Parity

Predictive Parity is a fairness metric in machine learning that requires the positive predictive value of a model to be equal across all protected groups, ensuring that the probability of a correct positive prediction is group-independent.

17

Publications

626

Citations

59

Authors

23

Institutions

Publications per year

2017–2026

13

Authors

59

  1. ML
    Michele Loi

    University of Zurich, Switzerland

    2 publications · 65 citations

    2

    h-index

  2. SY
    Samuel Yeom

    Carnegie Mellon University, United States

    2 publications · 32 citations

    2

    h-index

  3. MC
    Michael Carl Tschantz

    International Computer Science Institute, United States

    2 publications · 32 citations

    2

    h-index

  4. CH
    Christoph Heitz

    ZHAW Zurich University of Applied Sciences, Switzerland

    2 publications · 64 citations

    2

    h-index

  5. PG
    P. Gareth R. Seaward

    University of Toronto, Canada

    1 publications · 12 citations

    1

    h-index

1–5 of 59

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 25 citations

    2

    h-index

  2. Twin Research

    1 publications · 12 citations

    1

    h-index

  3. SSRN Electronic Journal

    Repository · 1 publications · 27 citations

    1

    h-index

  4. Journal of the American Medical Informatics Association

    JAMIA · 1 publications · 17 citations

    1

    h-index

  5. Behavioral Sciences & the Law

    1 publications · 52 citations

    1

    h-index

1–5