Authors : Vignesh K; Sharanjey G; Pranav R; Deepak Narees R; Muthukumaran K

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/2s3n98ph

Scribd : https://tinyurl.com/mu35fxre

DOI : https://doi.org/10.38124/ijisrt/25mar109

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.

Abstract : 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.

Keywords : Bias-Resistant AI, Retrieval-Augmented Generation (RAG), Equitable AI, Bias Intelligence Quotient (BiQ), Bias Mitigation, Clustering Algorithms, Content Diversification, Adaptive Learning, Fairness in AI, Ethical AI, Bias-Aware Retrieval, Explainable AI (XAI), Dynamic Bias Detection, Content Fairness, Adversarial Learning, Fair Content Generation, Geodesic Segmentation, Gaussian Mixture Models (GMM), Fair Information Retrieval, Context-Aware AI Systems.

References :

1. Oketunji, A., Anas, M., & Saina, D. (2023). Bias Neutralization Framework: Measuring Fairness in Large Language Models with Bias Intelligence Quotient (BiQ). arXiv preprint arXiv:2404.18276.
2. Hu, M., Wu, H., Guan, Z., Zhu, R., Guo, D., Qi, D., & Li, S. (2024). No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users. arXiv preprint arXiv:2410.07589.
3. IEEE. (2024). Responsible Artificial Intelligence and Bias Mitigation in Deep Learning Systems. IEEE Conference Publication.
4. Liu, H., Wang, W., Wang, Y., Liu, H., Liu, Z., & Tang, J. (2020). Mitigating Gender and Racial Bias in Text Generation Models with Adversarial Training. Proceedings of EMNLP.
5. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of ACM FAccT.
6. SkillReactor. (2024). Retrieval-Augmented Generation for AI-Generated Content: A Survey.
7. Sun, T., Gaut, A., Tang, S., et al. (2019). Mitigating Gender Bias in Natural Language Processing: A Literature Review. Proceedings of ACL.
8. Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608.
9. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys.
10. Geodesic Complexity via Fibered Decompositions of Cut Loci. Mescher, S., & Stegemeyer, M. (2022). arXiv preprint arXiv:2206.07691.
11. Quantification of Alveolar Recruitment for Mechanical Ventilation. Nabian, M., & Narusawa, U. (2024). Journal of Biomechanics.
12. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Lewis, P., Perez, E., Piktus, A., et al. (2021). NeurIPS.
13. Mitigating Unwanted Biases with Adversarial Learning. Zhang, B. H., Lemoine, B., & Mitchell, M. (2018). Proceedings of AAAI.
14. Zhong, S., Zeng, J., Yu, Y., Lin, B., & Anas, M. (2024). Clustering algorithms and RAG enhancing semi-supervised text classification with large LLMs. Preprint at arXiv.