State-of-the-art generative text-to-image models are known to exhibit social
biases and over-represent certain groups like people of perceived lighter skin
tones and men in their outcomes. In this work, we propose a method to mitigate
such biases and ensure that the outcomes are fair across different groups of
people. We do this by finetuning text-to-image models on synthetic data that
varies in perceived skin tones and genders constructed from diverse text
prompts. These text prompts are constructed from multiplicative combinations of
ethnicities, genders, professions, age groups, and so on, resulting in diverse
synthetic data. Our diversity finetuned (DFT) model improves the group fairness
metric by 150% for perceived skin tone and 97.7% for perceived gender. Compared
to baselines, DFT models generate more people with perceived darker skin tone
and more women. To foster open research, we will release all text prompts and
code to generate training images.

本研究提出了一种总结生成文本到图像模型中的社会偏见并确保结果在不同人群之间公平的方法。我们通过在多样的文本提示下，用由各种民族、性别、职业、年龄组等组合构建的感知肤色和性别差异的合成数据对文本到图像模型进行微调，从而减轻了这种偏见。相较于基准模型，我们的多样性微调模型在感知肤色和性别方面将群体公平指标分别提高了 150% 和 97.7%。该模型生成具有感知肤色较暗和更多女性的生成图像。为推动开放研究，我们将公开发布所有文本提示和生成训练图像的代码。