Bias Detection

Bias Detection is a methodological construct in the ethics of automated decision-making that identifies systematic distortions in algorithmic outcomes attributable to prejudicial assumptions or skewed data representations, thereby distinguishing it from general error detection by focusing specifically on fairness-related deviations rooted in historical, social, or technical inequities.

556

Publications

54.6K

Citations

1.7K

Authors

689

Institutions

Publications per year

2017–2026

358

Authors

1.7K

  1. SB
    Solon Barocas

    Cornell University, United States

    7 publications · 4.1K citations

    7

    h-index

  2. EF
    Emilio Ferrara

    University of Southern California, United States

    6 publications · 525 citations

    6

    h-index

  3. BG
    Béla Gipp

    University of Konstanz, Germany

    4 publications · 125 citations

    4

    h-index

  4. SM
    Sendhil Mullainathan

    University of Chicago, United States

    4 publications · 129 citations

    4

    h-index

  5. KL
    Kristian Lum

    Virginia Tech, United States

    4 publications · 154 citations

    4

    h-index

1–5 of 1.7K

Venues

  1. arXiv (Cornell University)

    Repository · 40 publications · 1.3K citations

    21

    h-index

  2. SSRN Electronic Journal

    Repository · 20 publications · 3K citations

    14

    h-index

  3. Communications of the ACM

    7 publications · 773 citations

    7

    h-index

  4. Machine Learning

    5 publications · 379 citations

    5

    h-index

  5. Science

    5 publications · 3.7K citations

    5

    h-index

1–5