AI生成的面孔影响性别刻板印象与种族同质化
AI-generated faces influence gender stereotypes and racial homogenization
AI总结：
本文系统记录了Stable Diffusion在种族、性别、职业和属性上的显著偏见及种族同质化现象，并提出可指定种族与性别分布的去偏方案，同时通过预注册实验证明包容性AI面孔能降低人类偏见，强调需解决文本到图像模型中的偏见问题。
摘要：
诸如Stable Diffusion之类的文本到图像生成式AI模型每天被全球数百万用户使用。然而，这些模型在多大程度上表现出种族和性别刻板印象尚未被充分理解。在此，我们记录了Stable Diffusion在六个种族、两种性别、32种职业和八种属性上的显著偏见。此外，我们考察了Stable Diffusion将同一种族的个体描绘得彼此相似的程度。该分析揭示了显著的种族同质化现象，例如，几乎将所有中东男性描绘为蓄须、棕色皮肤并穿着传统服饰。随后，我们提出了去偏解决方案，允许用户在生成图像时指定期望的种族和性别分布，同时尽量减少种族同质化。最后，通过一项预先注册的调查实验，我们发现证据表明，接触包容性的AI生成面孔会降低人们的种族和性别偏见，而接触非包容性的面孔则会增加此类偏见，无论这些图像是否被标注为AI生成。综合来看，我们的发现强调了解决文本到图像模型中偏见和刻板印象问题的必要性。
英文摘要：
Text-to-image generative AI models such as Stable Diffusion are used daily by millions worldwide. However, the extent to which these models exhibit racial and gender stereotypes is not yet fully understood. Here, we document significant biases in Stable Diffusion across six races, two genders, 32 professions, and eight attributes. Additionally, we examine the degree to which Stable Diffusion depicts individuals of the same race as being similar to one another. This analysis reveals significant racial homogenization, e.g., depicting nearly all Middle Eastern men as bearded, brown-skinned, and wearing traditional attire. We then propose debiasing solutions that allow users to specify the desired distributions of race and gender when generating images while minimizing racial homogenization. Finally, using a preregistered survey experiment, we find evidence that being presented with inclusive AI-generated faces reduces people's racial and gender biases, while being presented with non-inclusive ones increases such biases, regardless of whether the images are labeled as AI-generated. Taken together, our findings emphasize the need to address biases and stereotypes in text-to-image models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
