Gender bias is more exaggerated in online images than in text
成果类型：
Editorial Material
署名作者：
Hofstra, Bas; Mulders, Anne Maaike
署名单位：
Radboud University Nijmegen
刊物名称：
Nature
ISSN/ISSBN：
0028-5181
DOI：
10.1038/d41586-024-00291-6
发表日期：
2024-02-29
页码：
960-961
关键词：
摘要：
A big-data analysis shows that men are starkly over-represented in online images, and that gender bias is stronger in images compared with text. Such images could influence enduring gender biases in our offline lives. Biased images on the Internet could influence beliefs about gender.