Bias Resistant Retrieval Augmented Generation: A Clustering and BiQ Driven Approach with Equitable AI

In today's AI systems, ensuring fairness and reducing bias is more important than ever. Bias Resistant Retrieval- Augmented Generation: A Clustering and BiQ Driven Approach with Equitable AI introduces a smarter way to tackle bias in Retrieval-Augmented Generation systems. While RAG frameworks improve AI-generated content by blending external information with generative models, they often unintentionally reinforce biases, leading to unfair representations and stereotypes. To solve this, we propose Equitable AI an adaptive system that actively fights bias at every step. It uses a combination of a bias-aware retrieval process, a self-learning module that adapts to new forms of bias, and clustering techniques to ensure diverse and balanced content. At the heart of this system is the Bias Intelligence Quotient a powerful metric that tracks and reduces bias by measuring inclusivity, diversity, and fairness during both retrieval and generation. Bias Intelligence Quotient allows the system to adjust itself in real time, ensuring more balanced and equitable content. Our experiments show that this approach not only cuts down bias significantly but also increases content diversity and fairness, making it a crucial tool for ethically responsible AI in fields like healthcare, finance, and education.