  Vision-language models can encode societal biases and stereotypes, but there
are challenges to measuring and mitigating these multimodal harms due to
lacking measurement robustness and feature degradation. To address these
challenges, we investigate bias measures and apply ranking metrics for
image-text representations. We then investigate debiasing methods and show that
prepending learned embeddings to text queries that are jointly trained with
adversarial debiasing and a contrastive loss reduces various bias measures with
minimal degradation to the image-text representation.
