  The burgeoning influence of Large Language Models (LLMs) in shaping public
discourse and decision-making underscores the imperative to address inherent
biases within these AI systems. In the wake of AI's expansive integration
across sectors, addressing racial bias in LLMs has never been more critical.
This paper introduces a novel framework called Comprehensive Bias
Neutralization Framework (CBNF) which embodies an innovative approach to
quantifying and mitigating biases within LLMs. Our framework combines the Large
Language Model Bias Index (LLMBI) [Oketunji, A., Anas, M., Saina, D., (2023)]
and Bias removaL with No Demographics (BLIND) [Orgad, H., Belinkov, Y. (2023)]
methodologies to create a new metric called Bias Intelligence Quotient
(BiQ)which detects, measures, and mitigates racial bias in LLMs without
reliance on demographic annotations.
  By introducing a new metric called BiQ that enhances LLMBI with additional
fairness metrics, CBNF offers a multi-dimensional metric for bias assessment,
underscoring the necessity of a nuanced approach to fairness in AI [Mehrabi et
al., 2021]. This paper presents a detailed analysis of Latimer AI (a language
model incrementally trained on black history and culture) in comparison to
ChatGPT 3.5, illustrating Latimer AI's efficacy in detecting racial, cultural,
and gender biases through targeted training and refined bias mitigation
strategies [Latimer & Bender, 2023].
