Comparative Study of Gender Representation in AI-Generated News Images: Cross-Cultural Evidence from Chinese and Western Media Platforms

Jiong Li

doi:10.54691/5qdmep34

Authors

Jiong Li King’s College London University College London, "Department of Culture, Media & Creative Industries, Institute of Education, UCL", London, United Kingdom

DOI:

https://doi.org/10.54691/5qdmep34

Keywords:

Text-to-image generation, gender bias, cross-cultural comparison, AI-generated news images, algorithmic representation.

Abstract

The rapid adoption of text-to-image (T2I) tools in newsrooms raises pressing questions about the gendered visual conventions embedded in AI-generated imagery. This study investigates cross-cultural differences in gender representation through a controlled four-model comparative experiment. Two Chinese-origin models (ERNIE-ViLG, Tongyi Wanxiang) and two Western-origin models (DALL-E 3, Midjourney) generated 400 images across ten news-related occupations, coded along five dimensions of gender and visual presentation. Results reveal a cross-cultural gap in female representation, with Chinese-origin models producing female figures in 30.5% of cases versus 42.5% for Western-origin models, a divergence most pronounced in high-status occupations. Both ecosystems nevertheless shared stereotyped patterns in care-oriented roles and in the gendered encoding of visual authority through posture, attire, and age. These findings indicate that cross-cultural divergence operates primarily through the intensity of stereotyping rather than its presence, positioning model selection and prompt design as consequential cultural decisions for news organizations.

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