Bias Visualization

Bias Visualization is a methodological contribution within error visualization that specifically represents systematic deviations between model predictions and ground truth, distinguished by its focus on uncovering consistent, directional inaccuracies indicative of underlying model or data biases.

26

Publications

6.1K

Citations

75

Authors

32

Institutions

Publications per year

2017–2026

21

Authors

75

  1. DH
    Duen Horng Chau

    Georgia Institute of Technology, United States

    3 publications · 220 citations

    3

    h-index

  2. ÁA
    Ángel Alexander Cabrera

    Carnegie Mellon University, United States

    2 publications · 199 citations

    2

    h-index

  3. ED
    Evanthia Dimara

    Université Paris-Sud, France

    2 publications · 212 citations

    2

    h-index

  4. MC
    Michael Correll

    Tableau Software (United States), United States

    2 publications · 101 citations

    2

    h-index

  5. AB
    Anastasia Bezerianos

    Centre National de la Recherche Scientifique, France

    2 publications · 212 citations

    2

    h-index

1–5 of 75

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 4 publications · 266 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 199 citations

    2

    h-index

  3. International Journal of Computer Vision

    1 publications · 74 citations

    1

    h-index

  4. Nature Communications

    1 publications · 77 citations

    1

    h-index

  5. IEEE Transactions on Multimedia

    1 publications · 21 citations

    1

    h-index

1–5