Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings

Katja Geertruida Schmahl, Tom Julian Viering, Stavros Makrodimitris, Arman Naseri Jahfari, David Tax, Marco Loog


Abstract
Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain stereotypical gender biases. As a result, such unwanted biases will typically also be present in word embeddings derived from such corpora and downstream applications in the field of natural language processing (NLP). To minimize the effect of gender bias in these settings, more insight is needed when it comes to where and how biases manifest themselves in the text corpora employed. This paper contributes by showing how gender bias in word embeddings from Wikipedia has developed over time. Quantifying the gender bias over time shows that art related words have become more female biased. Family and science words have stereotypical biases towards respectively female and male words. These biases seem to have decreased since 2006, but these changes are not more extreme than those seen in random sets of words. Career related words are more strongly associated with male than with female, this difference has only become smaller in recently written articles. These developments provide additional understanding of what can be done to make Wikipedia more gender neutral and how important time of writing can be when considering biases in word embeddings trained from Wikipedia or from other text corpora.
Anthology ID:
2020.nlpcss-1.11
Volume:
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Month:
November
Year:
2020
Address:
Online
Editors:
David Bamman, Dirk Hovy, David Jurgens, Brendan O'Connor, Svitlana Volkova
Venue:
NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
94–103
Language:
URL:
https://aclanthology.org/2020.nlpcss-1.11
DOI:
10.18653/v1/2020.nlpcss-1.11
Bibkey:
Cite (ACL):
Katja Geertruida Schmahl, Tom Julian Viering, Stavros Makrodimitris, Arman Naseri Jahfari, David Tax, and Marco Loog. 2020. Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings. In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 94–103, Online. Association for Computational Linguistics.
Cite (Informal):
Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings (Schmahl et al., NLP+CSS 2020)
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PDF:
https://aclanthology.org/2020.nlpcss-1.11.pdf
Video:
 https://slideslive.com/38940605