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

Schmahl, Katja Geertruida; Viering, Tom J.; Makrodimitris, Stavros; Jahfari, Arman Naseri; Tax, David M. J.; Loog, Marco

doi:10.18653/v1/2020.nlpcss-1.11

Cited by 3 publications

(2 citation statements)

References 18 publications

(22 reference statements)

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“…13 The editors' community is consequently tackling this problem and fighting the gender gap. As recently shown by Schmahl et al (2020), these efforts pay off and not only more biographies about women are being added, but also NLP models such as word embeddings trained on Wikipedia articles are exhibiting less stereotypical biases.…”

Section: Discussionmentioning

confidence: 99%

“…In their following analysis, Wagner et al (2016) showed that gender inequalities can be observed also on other dimensions, e.g., women have to be more notable to have their biographies than men. These patterns occurring in Wikipedia have been shown to directly influence NLP models, such as word embeddings (Schmahl et al, 2020) or relation extraction (Gaut et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Assessing Gender Bias in Wikipedia: Inequalities in Article Titles

Faleńska¹,

Çetinoğlu²

2021

Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing

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Potential gender biases existing in Wikipedia's content can contribute to biased behaviors in a variety of downstream NLP systems. Yet, efforts in understanding what inequalities in portraying women and men occur in Wikipedia focused so far only on biographies, leaving open the question of how often such harmful patterns occur in other topics. In this paper, we investigate gender-related asymmetries in Wikipedia titles from all domains. We assess that for only half of gender-related articles, i.e., articles with words such as women or male in their titles, symmetrical counterparts describing the same concept for the other gender (and clearly stating it in their titles) exist. Among the remaining imbalanced cases, the vast majority of articles concern sports-and social-related issues. We provide insights on how such asymmetries can influence other Wikipedia components and propose steps towards reducing the frequency of observed patterns.Bias statement Inequalities in how men and women are represented in Wikipedia titles can be captured by NLP models and translate into biased behaviors creating representational harms (Blodgett et al., 2020). For example, if by default Wikipedia articles about national sports teams are about male teams, then a search engine might assume that a prototypical sportsperson is a man. Such a system when asked about famous volleyball players might exhibit recognition bias and return no women. Similarly, if Wikipedia provides special articles listing women photographers next to photographers, then an automatic knowledge extractor trained on such data might learn and propagate stereotypical generalizations that women within these occupations are an exception and should hold special qualities. Our work limits itself to binary gender values in extracting gender bias patterns from Wikipedia titles due to data scarcity. We acknow-ledge that not incorporating other values into our analysis indirectly causes recognition bias against non-binary people.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Gender Bias in Wikipedia: Inequalities in Article Titles

Faleńska¹,

Çetinoğlu²

2021

Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing

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Language variation and algorithmic bias: understanding algorithmic bias in British English automatic speech recognition

Markl

2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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All language is characterised by variation which language users employ to construct complex social identities and express social meaning. Like other machine learning technologies, speech and language technologies (re)produce structural oppression when they perform worse for marginalised language communities. Using knowledge and theories from sociolinguistics, I explore why commercial automatic speech recognition systems and other language technologies perform significantly worse for already marginalised populations, such as second-language speakers and speakers of stigmatised varieties of English in the British Isles. Situating language technologies within the broader scholarship around algorithmic bias, consider the allocative and representational harms they can cause even (and perhaps especially) in systems which do not exhibit predictive bias, narrowly defined as differential performance between groups. This raises the question whether addressing or "fixing" this "bias" is actually always equivalent to mitigating the harms algorithmic systems can cause, in particular to marginalised communities. CCS CONCEPTS• Computing methodologies → Natural language processing; Speech recognition.

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Countering underproduction of peer produced goods

Champion,

Hill

2024

New Media & Society

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Peer produced goods, such as online knowledge bases and free/libre open source software rely on contributors who often choose their tasks regardless of consumer needs. These goods are susceptible to underproduction: when popular goods are relatively low quality. Although underproduction is a common feature of peer production, very little is known about how to counteract it. We use a detailed longitudinal dataset from English Wikipedia to show that more experienced contributors—including those who contribute without an account—tend to contribute to underproduced goods. A within-person analysis shows that contributors’ efforts shift toward underproduced goods over time. These findings illustrate the value of retaining contributors in peer production, including those contributing without accounts, as a means to counter underproduction.

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

Cited by 3 publications

References 18 publications

Assessing Gender Bias in Wikipedia: Inequalities in Article Titles

Assessing Gender Bias in Wikipedia: Inequalities in Article Titles

Language variation and algorithmic bias: understanding algorithmic bias in British English automatic speech recognition

Countering underproduction of peer produced goods

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