Large language models (LLMs) are known to inherit biases from their training data, reflecting societal prejudices related to gender, race, and religion. While various debiasing techniques exist, they often struggle to eliminate these biases completely or come at the cost of reduced language model performance. Researchers are exploring a new approach called "unlearning," which aims to make AI models selectively forget biased or toxic information. One promising method involves masking toxic words within sentences and then training the model to minimize the likelihood of generating those masked words, effectively dissociating them from specific contexts. Early experiments using this masking technique on gender-biased text showed surprising results. Not only did it reduce gender bias effectively, but it also had a positive impact on mitigating biases related to race and religion, even though the unlearning process specifically targeted gender bias. This phenomenon, termed "transfer unlearning," suggests that debiasing efforts in one area can have broader, unintended benefits across other domains. This discovery opens exciting possibilities for developing more comprehensive and universal debiasing solutions for AI models. It raises questions about the interconnected nature of biases within these models and how targeted interventions might have wider-reaching impacts than previously thought. While this research is in its early stages, transfer unlearning offers a potential pathway toward creating fairer and more responsible AI systems. Further research is needed to explore the limitations of this approach, specifically regarding the reproducibility of mask rules and the issue of invalid unlearning of subsequent tokens in the sentence. Nonetheless, the initial findings offer a glimmer of hope in the ongoing quest to mitigate bias in AI and pave the way for more inclusive and equitable technological advancements.