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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Representativeness and face-ism: Gender bias in image search
Organization Unit
  • Roberto Ulloa
  • Ana Carolina Richter
  • Mykola Makhortykh
  • Aleksandra Urman
  • Celina Sylwia Kacperski
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title New Media & Society
Publisher Sage Publications
Geographical Reach international
ISSN 1461-4448
Volume 26
Number 6
Page Range 3541 - 3567
Date 2024
Abstract Text Implicit and explicit gender biases in media representations of individuals have long existed. Women are less likely to be represented in gender-neutral media content (representation bias), and their face-to-body ratio in images is often lower (face-ism bias). In this article, we look at representativeness and face-ism in search engine image results. We systematically queried four search engines (Google, Bing, Baidu, Yandex) from three locations, using two browsers and in two waves, with gender-neutral (person, intelligent person) and gendered (woman, intelligent woman, man, intelligent man) terminology, accessing the top 100 image results. We employed automatic identification for the individual’s gender expression (female/male) and the calculation of the face-to-body ratio of individuals depicted. We find that, as in other forms of media, search engine images perpetuate biases to the detriment of women, confirming the existence of the representation and face-ism biases. In-depth algorithmic debiasing with a specific focus on gender bias is overdue.
Free access at DOI
Digital Object Identifier 10.1177/14614448221100699
Other Identification Number merlin-id:22554
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Keywords Sociology and Political Science, Communication