This research proposes a comprehensive framework to identify and mitigate biases in Large Language Models (LLMs), which are increasingly used across diverse applications. Current LLMs often reflect positionality and stereotype biases, resulting in outputs that may harm or misrepresent certain groups, yet mitigating them is hindered by the complexity of their multi-stage training pipeline. Our project seeks to analyze biases throughout the LLM training and prediction pipeline, inspired by sociolinguistics and social psychology. We aim to investigate demographic representation in training datasets, examine stereotype acquisition, and assess biases in reasoning processes. By developing innovative tools like StereoLLMeter and persona-prompting frameworks, we aim to enhance LLM equity and performance. This high-risk, high-reward research could lead to fairer AI systems, driving positive societal and industrial applications while setting new standards in AI ethics and inclusivity.