Representativeness and face-ism: Gender bias in image search

• Published in: New media & society Vol. 26; no. 6; pp. 3541 - 3567
• Main Authors: Ulloa, Roberto, Richter, Ana Carolina, Makhortykh, Mykola, Urman, Aleksandra, Kacperski, Celina Sylwia
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-06-2024
• Subjects: gender bias; Algorithm auditing; search engines; face-ism; image search
• ISSN: 1461-4448; 1461-7315
• DOI: 10.1177/14614448221100699

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.