Data Annotation Ethics

Data Annotation Ethics is a methodological construct within Machine Translation Ethics that governs the principles and practices ensuring fairness, transparency, and accountability in the labeling, sourcing, and use of human-generated linguistic data for training and evaluating machine translation systems.

9

Publications

657

Citations

25

Authors

7

Institutions

Publications per year

2017–2026

8

Authors

25

  1. SS
    Shannon Spruit

    Delft University of Technology, Netherlands

    2 publications · 393 citations

    2

    h-index

  2. MD
    Mark Díaz

    Google (United States), United States

    2 publications · 92 citations

    2

    h-index

  3. RR
    Rachel Rosen

    Boston Children's Hospital, United States

    2 publications · 92 citations

    2

    h-index

  4. IK
    Ian Kivlichan

    Google (United States), United States

    2 publications · 92 citations

    2

    h-index

  5. DH
    Dirk Hovy

    Bocconi University, Italy

    2 publications · 393 citations

    2

    h-index

1–5 of 25

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 47 citations

    2

    h-index

  2. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 1 publications · 82 citations

    1

    h-index

  3. CHI Conference on Human Factors in Computing Systems

    Conference · 1 publications · 62 citations

    1

    h-index

  4. AI and Ethics

    1 publications · 45 citations

    1

    h-index

  5. 2022 ACM Conference on Fairness, Accountability, and Transparency

    Conference · 1 publications · 68 citations

    1

    h-index

1–5