Despite the remarkable performance of foundation vision-language models, the shared representation space for text and vision can also encode harmful label associations detrimental to fairness. While prior work has uncovered bias in vision-language models' (VLMs) classification performance across geography, work has been limited along the important axis of harmful label associations due to a lack of rich, labeled data. In this work, we investigate harmful label associations in the recently released Casual Conversations datasets containing more than 70,000 videos. We study bias in the frequency of harmful label associations across self-provided labels for age, gender, apparent skin tone, and physical adornments across several leading VLMs. We find that VLMs are $4-13$x more likely to harmfully classify individuals with darker skin tones. We also find scaling transformer encoder model size leads to higher confidence in harmful predictions. Finally, we find improvements on standard vision tasks across VLMs does not address disparities in harmful label associations.

尽管基础视觉语言模型的性能出色，但文本和图像的共享表示空间也可能编码有害的标签关联，对公平性产生不利影响。本研究通过对最近发布的包含超过70,000个视频的“Casual Conversations”数据集进行调查，发现视觉语言模型在年龄、性别、肤色和装饰品等方面的有害标签关联频率存在偏差。我们发现，较暗肤色的个体更有可能被视觉语言模型误分类，并且发现随着变压器编码器模型规模的增加，对有害预测的置信度也增加。最后，我们发现在标准视觉任务上的改进并不能解决有害标签关联的不平等问题。

视觉语言模型中有害标签关联的偏见