  We study the effectiveness of data-balancing for mitigating biases in
contrastive language-image pretraining (CLIP), identifying areas of strength
and limitation. First, we reaffirm prior conclusions that CLIP models can
inadvertently absorb societal stereotypes. To counter this, we present a novel
algorithm, called Multi-Modal Moment Matching (M4), designed to reduce both
representation and association biases (i.e. in first- and second-order
statistics) in multimodal data. We use M4 to conduct an in-depth analysis
taking into account various factors, such as the model, representation, and
data size. Our study also explores the dynamic nature of how CLIP learns and
unlearns biases. In particular, we find that fine-tuning is effective in
countering representation biases, though its impact diminishes for association
biases. Also, data balancing has a mixed impact on quality: it tends to improve
classification but can hurt retrieval. Interestingly, data and architectural
improvements seem to mitigate the negative impact of data balancing on
performance; e.g. applying M4 to SigLIP-B/16 with data quality filters improves
COCO image-to-text retrieval @5 from 86% (without data balancing) to 87% and
ImageNet 0-shot classification from 77% to 77.5%! Finally, we conclude with
recommendations for improving the efficacy of data balancing in multimodal
systems.
