@inproceedings{solmundsdottir-etal-2022-mean,
title = "Mean Machine Translations: On Gender Bias in {I}celandic Machine Translations",
author = {S{\'o}lmundsd{\'o}ttir, Agnes and
Gu{\dh}mundsd{\'o}ttir, Dagbj{\"o}rt and
Stef{\'a}nsd{\'o}ttir, Lilja Bj{\"o}rk and
Ingason, Anton},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.333",
pages = "3113--3121",
abstract = "This paper examines machine bias in language technology. Machine bias can affect machine learning algorithms when language models trained on large corpora include biased human decisions or reflect historical or social inequities, e.g. regarding gender and race. The focus of the paper is on gender bias in machine translation and we discuss a study conducted on Icelandic translations in the translation systems Google Translate and V{\'e}l{\th}{\'y}{\dh}ing.is. The results show a pattern which corresponds to certain societal ideas about gender. For example it seems to depend on the meaning of adjectives referring to people whether they appear in the masculine or feminine form. Adjectives describing positive personality traits were more likely to appear in masculine gender whereas the negative ones frequently appear in feminine gender. However, the opposite applied to appearance related adjectives. These findings unequivocally demonstrate the importance of being vigilant towards technology so as not to maintain societal inequalities and outdated views {---} especially in today{'}s digital world.",
}
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%0 Conference Proceedings
%T Mean Machine Translations: On Gender Bias in Icelandic Machine Translations
%A Sólmundsdóttir, Agnes
%A Gu\dhmundsdóttir, Dagbjört
%A Stefánsdóttir, Lilja Björk
%A Ingason, Anton
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F solmundsdottir-etal-2022-mean
%X This paper examines machine bias in language technology. Machine bias can affect machine learning algorithms when language models trained on large corpora include biased human decisions or reflect historical or social inequities, e.g. regarding gender and race. The focus of the paper is on gender bias in machine translation and we discuss a study conducted on Icelandic translations in the translation systems Google Translate and Vél\thý\dhing.is. The results show a pattern which corresponds to certain societal ideas about gender. For example it seems to depend on the meaning of adjectives referring to people whether they appear in the masculine or feminine form. Adjectives describing positive personality traits were more likely to appear in masculine gender whereas the negative ones frequently appear in feminine gender. However, the opposite applied to appearance related adjectives. These findings unequivocally demonstrate the importance of being vigilant towards technology so as not to maintain societal inequalities and outdated views — especially in today’s digital world.
%U https://aclanthology.org/2022.lrec-1.333
%P 3113-3121
Markdown (Informal)
[Mean Machine Translations: On Gender Bias in Icelandic Machine Translations](https://aclanthology.org/2022.lrec-1.333) (Sólmundsdóttir et al., LREC 2022)
ACL