With the increasing integration of Artificial Intelli- gence (AI) in various applications, concerns about fairness and bias have become paramount. While numerous strategies have been proposed to miti- gate bias, there is a significant gap in the litera- ture regarding the use of Large Language Mod- els (LLMs) in these techniques. This paper aims to bridge this gap by presenting an innovative approach that incorporates LLMs for bias mitigation and ensuring fairness in AI systems. Our proposed method, built on previous research, is designed to be model and system-agnostic, while keeping humans in the loop. We envision these approaches to foster trust between AI developers and end-users/stakeholders, contributing to the discourse on responsible AI.