  The Large Language Model Bias Index (LLMBI) is a pioneering approach designed
to quantify and address biases inherent in large language models (LLMs), such
as GPT-4. We recognise the increasing prevalence and impact of LLMs across
diverse sectors. This research introduces a novel metric, LLMBI, to
systematically measure and mitigate biases potentially skewing model responses.
We formulated LLMBI using a composite scoring system incorporating multiple
dimensions of bias, including but not limited to age, gender, and racial
biases. To operationalise this metric, we engaged in a multi-step process
involving collecting and annotating LLM responses, applying sophisticated
Natural Language Processing (NLP) techniques for bias detection, and computing
the LLMBI score through a specially crafted mathematical formula. The formula
integrates weighted averages of various bias dimensions, a penalty for dataset
diversity deficiencies, and a correction for sentiment biases. Our empirical
analysis, conducted using responses from OpenAI's API, employs advanced
sentiment analysis as a representative method for bias detection. The research
reveals LLMs, whilst demonstrating impressive capabilities in text generation,
exhibit varying degrees of bias across different dimensions. LLMBI provides a
quantifiable measure to compare biases across models and over time, offering a
vital tool for systems engineers, researchers and regulators in enhancing the
fairness and reliability of LLMs. It highlights the potential of LLMs in
mimicking unbiased human-like responses. Additionally, it underscores the
necessity of continuously monitoring and recalibrating such models to align
with evolving societal norms and ethical standards.
