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Title: Chameleon: Foundation Models for Fairness-Aware Multi-Modal Data Augmentation to Enhance Coverage of Minorities
Potential harms from the under-representation of minorities in 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 generative AI advancements, large language 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 dataset with minimal addition of synthetically generated tuples to enhance the coverage of the under-represented groups. Our system applies quality and outlier-detection tests to ensure the quality and semantic integrity of the generated tuples. In order to minimize the rejection chance of the generated tuples, we propose multiple strategies to provide a guide for the foundation model. Our experiment results, in addition to confirming the efficiency of our proposed algorithms, illustrate our approach's effectiveness, as the model's unfairness in a downstream task significantly dropped after data repair using Chameleon.  more » « less
Award ID(s):
2107290 2348919 2312931 2106176
PAR ID:
10554109
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the VLDB Endowment
Volume:
17
Issue:
11
ISSN:
2150-8097
Page Range / eLocation ID:
3470 to 3483
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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