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.

Search engine personalization, while enhancing user satisfaction, can lead to information disparities. Previous studies on this topic face limitations, such as the absence of context-aware data collection, superficial URL-level analysis, and human-dependent annotations. We propose FAIR-SE, a Framework for Analyzing Information dispaRities in Search Engines that addresses these challenges through AWS Lambda-based concurrent data collection and LLM-generated persona-based content analysis. We collected search results across four user contexts (Search History, Geo-location, Language Preference, and Access Environment) and analyzed them through four analytical perspectives (Political Leaning, Topic-specific Stance, Subjectivity, and Bias). Experiments conducted on two globally prominent search engines across nine controversial topics demonstrate the efficacy of FAIR-SE regarding benchmark accuracy, persona consistency, and ability to reflect real-world discourse patterns across diverse topics. Our statistical analysis identifies distinct search engine characteristics and demonstrates significant information disparities in our case studies examining regional disparities in search results. Our code and datasets are publicly available at: https://github.com/bigbases/FAIR-SE.