Structured Reasoning for Fairness: A Multi-Agent Approach to Bias Detection in Textual Data

• URL: http://arxiv.org/abs/2503.00355v1
• Date: Sat, 01 Mar 2025 05:27:54 GMT
• Title: Structured Reasoning for Fairness: A Multi-Agent Approach to Bias Detection in Textual Data
• Authors: Tianyi Huang, Elsa Fan,
• Abstract summary: We propose a multi-agent framework that identifies by disentangling each statement as fact or opinion.<n>By combining enhanced detection accuracy with interpretable explanations, this approach promotes accountability in modern language models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: From disinformation spread by AI chatbots to AI recommendations that inadvertently reinforce stereotypes, textual bias poses a significant challenge to the trustworthiness of large language models (LLMs). In this paper, we propose a multi-agent framework that systematically identifies biases by disentangling each statement as fact or opinion, assigning a bias intensity score, and providing concise, factual justifications. Evaluated on 1,500 samples from the WikiNPOV dataset, the framework achieves 84.9% accuracy$\unicode{x2014}$an improvement of 13.0% over the zero-shot baseline$\unicode{x2014}$demonstrating the efficacy of explicitly modeling fact versus opinion prior to quantifying bias intensity. By combining enhanced detection accuracy with interpretable explanations, this approach sets a foundation for promoting fairness and accountability in modern language models.

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