Generative Artificial Intelligence (AI) models, especially large language models (LLMs), are increasingly used to retrieve and generate information in digital libraries. However, these models often reflect cultural biases and stereotypes that distort or marginalize knowledge representations. This paper tackles bias in LLM-generated English text on Asian history and culture. We formally define bias categories, including stereotyping, omission, ethnocentrism, and simplification, in the context of generative AI outputs. We propose a novel framework combining multi-perspective generation with bias detection to mitigate such biases. Supported by a theoretical analysis, we introduce formal bias measures and prove that under ideal conditions, our method can eliminate stereotypical content and perspective omissions. Furthermore, we present a bias annotation scheme and algorithm that generates answers incorporating diverse cultural viewpoints while filtering out identified stereotypes. Our approach provides formal guarantees for bias reduction, advancing the state-of-the-art by bridging bias mitigation, information retrieval, and digital library research to promote fairness and cultural inclusivity in AI-generated content.