Abstract
As we increasingly use Artificial Intelligence (AI) in decision-making for industries like healthcare, finance, e-commerce, and even entertainment, it is crucial to also reflect on the ethical aspects of AI, for example the inclusivity and fairness of the information it provides. In this work, we aimed to evaluate different text-to-image AI models and compare the degree of gender bias they present. The evaluated models were Stable Diffusion XL (SDXL), Stable Diffusion Cascade (SC), DALL-E and Emu. We hypothesized that DALL-E and Stable Diffusion, which are comparatively older models, would exhibit a noticeable degree of gender bias towards men, while Emu, which was recently released by Meta AI, would have more balanced results. As hypothesized, we found that both Stable Diffusion models exhibit a noticeable degree of gender bias while Emu demonstrated more balanced results (i.e. less gender bias). However, interestingly, Open AI's DALL-E exhibited almost opposite results, such that the ratio of women to men was significantly higher in most cases tested. Here, although we still observed a bias, the bias favored females over males. This bias may be explained by the fact that OpenAI changed the prompts at its backend, as observed during our experiment. We also observed that Emu from Meta AI utilized user information while generating images via WhatsApp. We also proposed some potential solutions to avoid such biases, including ensuring diversity across AI research teams and having diverse datasets.
Abstract (translated)
随着我们在医疗、金融、电子商务乃至娱乐等行业中越来越多地使用人工智能（AI）进行决策，反思AI的伦理方面变得至关重要，例如它所提供的信息是否具有包容性和公平性。在这项工作中，我们旨在评估不同的文本到图像的AI模型，并比较它们所呈现的性别偏见程度。被评估的模型包括Stable Diffusion XL (SDXL)、Stable Diffusion Cascade (SC)、DALL-E和Emu。我们假设较为老的模型如DALL-E和Stable Diffusion会表现出明显的倾向于男性的性别偏见，而Meta AI最近发布的Emu则会有更加平衡的结果。 正如我们的假设所预测的那样，我们发现两个Stable Diffusion模型确实存在明显的性别偏见，而Emu的表现更为均衡（即性别偏见较少）。然而有趣的是，OpenAI的DALL-E表现出几乎相反的结果，在大多数测试案例中女性与男性的比例明显较高。在这里虽然依然观察到了某种偏差，但这种偏差倾向于有利于女性而非男性。这一现象可能是因为在我们的实验过程中，我们注意到OpenAI在其后台修改了提示词。 此外，我们还发现Meta AI的Emu在通过WhatsApp生成图像时利用了用户信息。基于上述研究结果，我们也提出了一些避免此类偏见的潜在解决方案，包括确保人工智能研究团队的多样性以及使用多样化的数据集。
URL
https://arxiv.org/abs/2509.08004
