Machine Scoring Fairness

Machine Scoring Fairness is a methodological construct in educational assessment that ensures automated scoring systems do not systematically disadvantage individuals or groups based on irrelevant attributes such as race, gender, or socioeconomic background.

6

Publications

328

Citations

14

Authors

6

Institutions

Publications per year

2017–2026

5

Variants

Algorithmic Scoring EquityAutomated Assessment Fairness

Authors

14

  1. YA
    Yigal Attali

    Educational Testing Service, United States

    1 publications · 139 citations

    1

    h-index

  2. KI
    Kori Inkpen

    Microsoft (United States), United States

    1 publications · 27 citations

    1

    h-index

  3. ST
    Sarah Tan

    Cornell University, United States

    1 publications · 27 citations

    1

    h-index

  4. EK
    Ece Kamar

    Microsoft (United States), United States

    1 publications · 27 citations

    1

    h-index

  5. FL
    Franziska Leutner

    University College London, United Kingdom

    1 publications · 21 citations

    1

    h-index

1–5 of 14

Venues

  1. Information Technology for Development

    1 publications · 47 citations

    1

    h-index

  2. Frontiers in Psychology

    1 publications · 21 citations

    1

    h-index

  3. Applied Measurement in Education

    1 publications · 139 citations

    1

    h-index

  4. Journal of Science Education and Technology

    1 publications · 82 citations

    1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 27 citations

    1

    h-index

1–5