Summary of Chameleon: Foundation Models For Fairness-aware Multi-modal Data Augmentation to Enhance Coverage Of Minorities, by Mahdi Erfanian and H. V. Jagadish and Abolfazl Asudeh
Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities
by Mahdi Erfanian, H. V. Jagadish, Abolfazl Asudeh
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel system called Chameleon that utilizes generative AI tools to augment training data and enhance the representation of minority groups. Specifically, it uses large language models and foundation models to generate synthetic tuples that mimic the distribution of under-represented groups. The system employs a rejection sampling approach to ensure high-quality generated data, with multiple strategies for guiding the foundation model to minimize rejections. Experimental results demonstrate the effectiveness of Chameleon in reducing unfairness in downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chameleon is a new way to make sure that artificial intelligence models are fair and don’t favor one group over another. Right now, AI models can be biased because they’re trained on data that doesn’t represent everyone equally. This problem is especially important when dealing with images, sounds, and text together (multi-modal settings). The researchers propose a system that uses powerful language models to create new, fake examples that are similar to the real data but better represent under-represented groups. They test this approach and show that it helps reduce unfairness in AI models. |
Keywords
* Artificial intelligence * Multi modal
