Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities

Understand

The potential harms of the under-representation of minorities in training data, particularly in multi-modal settings, is a well-recognized concern.

• While there has been extensive effort in detecting such under-representation, resolution has remained a challenge.
• With recent advancements in generative AI, large language models and foundation models have emerged as versatile tools across various domains.
• In this paper, we propose Chameleon, a system that efficiently utilizes these tools to augment a data set with a minimal addition of synthetically generated tuples, in order to enhance the coverage of the under-represented groups.