FAIR-RAG: An End-to-End Framework for Mitigating Political Bias through Fair Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) systems can amplify political bias from underlying web corpora. To empirically demonstrate this amplification, we first analyze 16,254 documents from the C4 dataset and 24,300 LLM-generated responses, revealing significant left-leaning and supportive stance bias that can propagate strongly from retrieval to generation. To mitigate this amplification of political bias, we propose FAIR-RAG, an end-to-end framework integrating (1) multi-LLM persona-based annotation, (2) a vector database with political-stance metadata, and (3) a multi-stage fairness engine designed for each of the three stages in RAG systems. FAIR-RAG achieves Attention Weighted Rank Fairness of 97.51 (82.1% improvement) and Perspective Balance of 51.01/82.37 (average 5.6% improvement over state-of-the-art) while maintaining high output quality (Context Precision: 0.974/0.975, Faithfulness: 0.994/0.996). Ablation studies confirm that all three components must operate collaboratively for optimal bias mitigation. This work provides a foundational framework for developing trustworthy and equitable AI information systems. All source code and experimental scripts are publicly available at: https://github.com/bigbases/FAIR-RAG.