AI生成图像强化职业性别刻板印象，需警惕偏见传播
The Digital Mirror: Gender Bias and Occupational Stereotypes in AI-Generated Images
- 用750张职业画像测试DALL-E 3和Ideogram的性别偏见
- 两类工具均强化传统性别角色，程度不同
- 适合关注AI伦理与公平性的研究者和从业者
生成式AI在图像创作中潜力巨大，但现有研究多聚焦生成过程与画质，忽视了表征偏差。本研究通过超过750张职业相关提示生成的AI图像，对比分析DALL-E 3与Ideogram在性别偏见方面的表现。主题分析显示，两款工具均在不同程度上强化了传统性别角色刻板印象，尤其体现在职业形象的呈现上。研究还探讨了年龄与情绪在生成图像中的表现。结果表明，当前AI视觉工具可能加剧社会刻板印象，影响媒体与职场中的多元代表性。论文最后提出针对实践者、用户及研究者的改进建议，以提升生成图像中性别可见性与多样性。
原文摘要 · Abstract (English)
Generative AI offers vast opportunities for creating visualisations, such as graphics, videos, and images. However, recent studies around AI-generated visualisations have primarily focused on the creation process and image quality, overlooking representational biases. This study addresses this gap by testing representation biases in AI-generated pictures in an occupational setting and evaluating how two AI image generator tools, DALL-E 3 and Ideogram, compare. Additionally, the study discusses topics such as ageing and emotions in AI-generated images. As AI image tools are becoming more widely used, addressing and mitigating harmful gender biases becomes essential to ensure diverse representation in media and professional settings. In this study, over 750 AI-generated images of occupations were prompted. The thematic analysis results revealed that both DALL-E 3 and Ideogram reinforce traditional gender stereotypes in AI-generated images, although to varying degrees. These findings emphasise that AI visualisation tools risk reinforcing narrow representations. In our discussion section, we propose suggestions for practitioners, individuals and researchers to increase representation when generating images with visible genders.
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