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Online images amplify gender bias
Douglas Guilbeault1, Solène Delecourt2, Tasker Hull3
1Haas School of Business, University of California, Berkeley, Berkeley, CA, USA. douglas.guilbeault@haas.berkeley.edu.
Nature
|February 14, 2024
Summary
Online images significantly worsen gender bias compared to text. This visual shift amplifies stereotypes, particularly affecting women's representation and perception in various social categories.
Area of Science:
- Social Psychology
- Digital Media Studies
- Computer Science
Background:
- Increasing online image consumption over text.
- Visual content dominates social media and news.
- Images are processed faster and more memorably than text.
Purpose of the Study:
- To investigate how the rise of online images affects gender bias.
- To compare gender bias prevalence in images versus text.
- To assess the psychological impact of visual search on gender stereotypes.
Main Methods:
- Analysis of over one million images and billions of words from Google, Wikipedia, and IMDb across 3,495 social categories.
- Comparison of gender bias in images and text data.
- A nationally representative experiment on the effect of image-based vs. text-based occupational searches.
Main Results:
- Gender bias is consistently more prevalent in images than in text for both male- and female-typed categories.
- Online image underrepresentation of women is significantly worse than in text, public opinion, or census data.
- Searching for images of occupations amplifies gender bias in beliefs more than text searches.
Conclusions:
- The shift to visual communication online exacerbates gender bias.
- Addressing visual gender bias is crucial for an inclusive internet.
- Interventions are needed to mitigate the amplified stereotypes in visual online content.
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