The Bias of Harmful Label Associations in Vision-Language Models

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 …

bias classification cs.cv encode fairness foundation geography language language models performance prior representation space text vision vision-language models vlms work