LLM-Guided Synthetic Augmentation (LGSA) for Mitigating Bias in AI Systems

This is the preprint version of the article "LLM - Guided Synthetic Augmentation (LGSA) for Mitigating Bias in AI Systems." This version is made available on arXiv for early dissemination. If accepted, the final authenticated version will be published in the respective venue. Dr. G opichand G School of Computer Science and Engineering Vellore Institute of Technology Vellore - 632014, TamilNadu, India gopichand.g@vit.ac.in Abstract -- Bias in Artificial Intelligence systems, especially those that rely on natural language data, brings up serious ethical and practical issues. When certain groups are underrepresented, it often leads to uneven performance across different demographics. Whil e traditional fairness methods like pre - processing, in - processing, and post - processing can be helpful, they usually depend on protected - attribute labels, create a trade - off between accuracy and fairness, and struggle to adapt across various datas ets. To tackle these challenges, this study presents LLM - Guided Synthetic Augmentation (LGSA), a process that leverages large language models to create counterfactual examples for underrepresented groups while keeping label integrity intact. We put LGSA to the test on a controlled dataset of short English sentences that included gendered pronouns, professions, and binary task labels. The process involved using structured prompts to a large language model to generate gender - swapped paraphrases, followed by a thorough quality control process. This included checking for semantic similarity, verifying attributes, screening for toxi city, and conducting human spot checks. The augmented dataset broadened training coverage and was utilized to train a classifier under consistent experimental conditions. The results showed that LGSA significantly lessens performance disparities without co mpromising accuracy. The baseline model achieved an impressive 96.7% accuracy but had a gender bias gap of 7.2%. A simple swap augmentation brought the gap down to 0.7% but also reduced accuracy to 95.6%. In contrast, LGSA achieved an overall accuracy of 9 9.1%, showing strong performance on female - labeled examples and a reduced gap of 1.9%. These results indicate that LGSA is a powerful and dependable strategy for mitigating bias. By generating diverse and semantically accurate counterfactuals, this method enhances the balance of subgroup performance, narrows bias gaps, and maintains high ove rall task accuracy and label fidelity, showcasing its potential as a practical framework for fairness - focused AI systems.