Abstract
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.
Abstract (translated)
生成式AI为创建可视化内容（如图形、视频和图像）提供了巨大的机会。然而，最近关于AI生成的可视化研究主要集中在创作过程和图像质量上，而忽略了代表性偏见。本研究旨在填补这一空白，通过测试在职业环境中的AI生成图片的代表性偏见，并评估两种AI图像生成工具——DALL-E 3和Ideogram之间的差异。 此外，该研究还讨论了年龄和情感等主题在AI生成图像中的表现情况。随着AI图像工具被越来越广泛地使用，解决并缓解有害性别偏见变得至关重要，以确保媒体和专业环境中具有多样化的代表性。在这项研究中，触发了超过750张不同职业的AI生成图片。主题分析结果表明，DALL-E 3和Ideogram在生成包含明确性别的图像时都会强化传统的性别刻板印象，尽管程度有所不同。 这些发现强调了AI可视化工具可能会固化狭隘的表现形式的风险。在我们的讨论部分中，我们为从业者、个人以及研究人员提出了建议，旨在提高在生成具有可见性别的图像时的代表性多样性。
URL
https://arxiv.org/abs/2510.08628
