Predictive Bias Analysis

Predictive Bias Analysis is a methodological framework within fairness review that systematically evaluates whether a predictive model's outputs exhibit differential performance or error rates across socially salient groups, thereby identifying inequities arising from algorithmic decision-making.

98

Publications

5.5K

Citations

299

Authors

151

Institutions

Publications per year

2017–2026

52

Authors

299

  1. EY
    Emre Yoldaş

    Federal Reserve Board of Governors, United States

    3 publications · 38 citations

    3

    h-index

  2. DB
    Deniz Baglan

    Howard University, United States

    3 publications · 38 citations

    3

    h-index

  3. RL
    Robert L. Linn

    University of Illinois Urbana-Champaign, United States

    3 publications · 181 citations

    3

    h-index

  4. AC
    Alexandra Chouldechova

    Carnegie Mellon University, United States

    3 publications · 194 citations

    3

    h-index

  5. SM
    Stephen M. Smith

    University of Oxford, United Kingdom

    2 publications · 544 citations

    2

    h-index

1–5 of 299

Venues

  1. arXiv (Cornell University)

    Repository · 15 publications · 954 citations

    13

    h-index

  2. Journal of Educational Measurement

    4 publications · 219 citations

    4

    h-index

  3. Journal of Applied Psychology

    3 publications · 210 citations

    3

    h-index

  4. Water Resources Research

    2 publications · 242 citations

    2

    h-index

  5. BMC Bioinformatics

    2 publications · 92 citations

    2

    h-index

1–5