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Representativeness and face-ism: Gender bias in image search
Roberto Ulloa1, Ana Carolina Richter2, Mykola Makhortykh3
1GESIS-Leibniz Institute for the Social Sciences, Germany.
New Media & Society
|May 22, 2024
Summary
Search engine images show gender bias, with fewer women represented and smaller face-to-body ratios (face-ism). Algorithmic debiasing is needed to address these representation and face-ism biases.
Area of Science:
- Media Studies
- Computer Science
- Sociology
Background:
- Gender biases are prevalent in media representations.
- Women face underrepresentation (representation bias) and skewed image proportions (face-ism bias).
Purpose of the Study:
- To investigate representation and face-ism biases in search engine image results.
- To determine if search engines perpetuate gender biases found in other media.
Main Methods:
- Systematic querying of four search engines (Google, Bing, Baidu, Yandex) using gender-neutral and gendered terms.
- Analysis of the top 100 image results for gender expression and face-to-body ratio using automatic identification.
Main Results:
- Search engine images confirm representation bias, showing fewer women in results for gender-neutral terms.
- Face-ism bias was also confirmed, with women depicted having lower face-to-body ratios.
- These biases mirror those found in broader media contexts.
Conclusions:
- Search engine image results perpetuate harmful gender biases against women.
- There is an urgent need for algorithmic debiasing, specifically targeting gender bias in image search results.
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