Online images amplify gender bias

• 成果类型: Article
• 署名作者: Guilbeault, Douglas; Delecourt, Solene; Hull, Tasker; Desikan, Bhargav Srinivasa; Chu, Mark; Nadler, Ethan
• 署名单位: University of California System; University of California Berkeley; Columbia University; University of Southern California
• 刊物名称: Nature
• ISSN/ISSBN: 0028-4201
• DOI: 10.1038/s41586-024-07068-x
• 发表日期: 2024-07-01
• 页码: 1049-+
• 关键词: stereotypes; engagement; culture; science; words

摘要

Each year, people spend less time reading and more time viewing images(1), which are proliferating online(2-4). Images from platforms such as Google and Wikipedia are downloaded by millions every day(2,5,6), and millions more are interacting through social media, such as Instagram and TikTok, that primarily consist of exchanging visual content. In parallel, news agencies and digital advertisers are increasingly capturing attention online through the use of images(7,8), which people process more quickly, implicitly and memorably than text(9-12). Here we show that the rise of images online significantly exacerbates gender bias, both in its statistical prevalence and its psychological impact. We examine the gender associations of 3,495 social categories (such as 'nurse' or 'banker') in more than one million images from Google, Wikipedia and Internet Movie Database (IMDb), and in billions of words from these platforms. We find that gender bias is consistently more prevalent in images than text for both female- and male-typed categories. We also show that the documented underrepresentation of women online(13-18) is substantially worse in images than in text, public opinion and US census data. Finally, we conducted a nationally representative, preregistered experiment that shows that googling for images rather than textual descriptions of occupations amplifies gender bias in participants' beliefs. Addressing the societal effect of this large-scale shift towards visual communication will be essential for developing a fair and inclusive future for the internet.