This paper explores the potential of Large Language Models (LLMs) in achieving fairness in classification tasks through in-context learning. The authors introduce a framework that outlines fairness regulations aligned with various fairness definitions, each modulated by varying degrees of abstraction. They investigate the configuration for in-context learning and the procedure for selecting in-context demonstrations using Retrieval-Augmented Generation (RAG). Experiments conducted with different LLMs, including GPT-4, LLaMA, and Gemini, indicate that GPT-4 delivers superior results in terms of both accuracy and fairness compared to other models. The study highlights the importance of providing additional context and defining fairness criteria to improve the fairness of LLMs' outcomes. The paper also discusses the limitations and ethical considerations of the research, suggesting areas for further exploration, such as broader datasets and different LLMs.This paper explores the potential of Large Language Models (LLMs) in achieving fairness in classification tasks through in-context learning. The authors introduce a framework that outlines fairness regulations aligned with various fairness definitions, each modulated by varying degrees of abstraction. They investigate the configuration for in-context learning and the procedure for selecting in-context demonstrations using Retrieval-Augmented Generation (RAG). Experiments conducted with different LLMs, including GPT-4, LLaMA, and Gemini, indicate that GPT-4 delivers superior results in terms of both accuracy and fairness compared to other models. The study highlights the importance of providing additional context and defining fairness criteria to improve the fairness of LLMs' outcomes. The paper also discusses the limitations and ethical considerations of the research, suggesting areas for further exploration, such as broader datasets and different LLMs.