Preprocessing Bias Mitigation

Preprocessing Bias Mitigation is a method in algorithmic fairness that modifies training data to reduce biases with respect to sensitive attributes before model learning occurs, distinguishing itself from in-processing and post-processing approaches by addressing bias at the data transformation stage.

20

Publications

3K

Citations

61

Authors

23

Institutions

Publications per year

2017–2026

14

Authors

61

  1. VK
    Vijay Keswani

    Yale University, United States

    2 publications · 41 citations

    2

    h-index

  2. NK
    Nisheeth K. Vishnoi

    Yale University, United States

    2 publications · 41 citations

    2

    h-index

  3. JS
    Julia Stoyanovich

    New York University, United States

    2 publications · 28 citations

    2

    h-index

  4. SS
    Sebastian Schelter

    University of Amsterdam, Netherlands

    2 publications · 28 citations

    2

    h-index

  5. FK
    Faisal Kamiran

    Information Technology University, Pakistan

    1 publications · 1.3K citations

    1

    h-index

1–5 of 61

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 63 citations

    3

    h-index

  2. Knowledge and Information Systems

    2 publications · 1.4K citations

    2

    h-index

  3. UvA-DARE (University of Amsterdam)

    Repository · 2 publications · 28 citations

    2

    h-index

  4. Knowledge-Based Systems

    1 publications · 352 citations

    1

    h-index

  5. Expert Systems with Applications

    1 publications · 312 citations

    1

    h-index

1–5