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
Parents
9
The Social Impact of Natural Language Processing
Dirk Hovy, Shannon Spruit · 2016 · 311 citations
Mark Díaz, Ian Kivlichan, Rachel Rosen et al. · 2022 ACM Conference on Fairness, Accountability, and Transparency · 2022 · 68 citations · Full text
1–5 of 9
25
2
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2
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2
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2
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2
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1–5 of 25
7
Center for Applied Linguistics
Washington D.C., United States
1 h-index
1 publications
311 citations
Fondazione Bruno Kessler
Trento, Italy
1 h-index
2 publications
34 citations
Google (United States)
Mountain View, United States
1 h-index
6 publications
408 citations
Sony Corporation (United States)
New York, United States
1 h-index
2 publications
50 citations

Cornell University
Ithaca, United States
1 h-index
2 publications
50 citations
Politecnico di Torino
Turin, Italy
1 h-index
3 publications
51 citations
George Washington University
Washington, United States
1 h-index
1 publications
45 citations
1–7 of 7
Repository · 2 publications · 47 citations
2
h-index
Repository · 1 publications · 82 citations
1
h-index
Conference · 1 publications · 62 citations
1
h-index
1 publications · 45 citations
1
h-index
Conference · 1 publications · 68 citations
1
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1–5