Large language models (LLMs) like ChatGPT are trained on massive amounts of text data, and unfortunately, that data often reflects existing societal biases. While developers work hard to make these AIs appear unbiased, a new study reveals how these biases can hide within the models themselves, subtly influencing their responses. Researchers have identified what they call the 'implicit bias problem,' where LLMs, when prompted to role-play different demographics, demonstrate varying levels of competency in the same tasks. For instance, an LLM might struggle to answer technical questions when impersonating a woman, reflecting harmful stereotypes. Even when these models give seemingly unbiased answers on sensitive topics, their performance in different simulated roles can expose inconsistencies. This research introduces a novel framework called Bayesian Theory-based Bias Removal (BTBR). BTBR uses a clever technique to identify the most biased data points in public datasets, effectively pinpointing the information that contributes to the implicit bias problem. By converting this biased information into a structured format and then using model editing techniques, the researchers were able to 'unlearn' the bias, directly modifying the model's internal workings. Experiments showed that BTBR significantly improved the fairness of LLMs across various tasks, from answering knowledge-based questions to solving math problems, while also minimizing performance degradation. This study sheds light on the complex challenges of achieving true fairness in AI and provides a promising new path towards building more equitable and unbiased language models. The ongoing quest to eliminate bias requires refining methods like BTBR, especially by identifying higher-quality bias datasets. Future research will also focus on tackling multiple biases simultaneously, furthering the goal of making AI a force for greater equality.