Stereotype Amplification
Stereotype Amplification is a systematic bias in machine translation whereby gender, racial, or cultural stereotypes present in training data are reinforced or intensified in translated outputs, leading to the propagation of distorted social representations.
26
Publications
3.9K
Citations
75
Authors
35
Institutions
Publications per year
2017–2026
20
Parents
26
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou et al. · arXiv (Cornell University) · 2016 · 1.4K citations · Full text
Measuring Bias in Contextualized Word Representations
Keita Kurita, Nidhi Vyas, Ayush Pareek et al. · 2019 · 339 citations · Full text
Gender bias and stereotypes in Large Language Models
Hadas Kotek, Rikker Dockum, David Sun · 2023 · 328 citations · Full text
Social Sciences, Social Psychology, Computational Linguistics +16
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35
Carnegie Mellon University
Pittsburgh, United States
3 h-index
13 publications
1.9K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
3 h-index
3 publications
272 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
4 publications
452 citations
University of Washington
Seattle, United States
2 h-index
3 publications
461 citations

University of Toronto
Toronto, Canada
2 h-index
4 publications
240 citations

Bocconi University
Milan, Italy
2 h-index
4 publications
533 citations

Johns Hopkins University
Baltimore, United States
2 h-index
2 publications
226 citations
National Research Council
Rome, Italy
1 h-index
2 publications
158 citations
1–8 of 35
Repository · 7 publications · 1.6K citations
7
h-index
2 publications · 384 citations
2
h-index
AJE · 1 publications · 11 citations
1
h-index
1 publications · 15 citations
1
h-index
Conference · 1 publications · 58 citations
1
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1–5