  Employing Large Language Models (LLM) in various downstream applications such
as classification is crucial, especially for smaller companies lacking the
expertise and resources required for fine-tuning a model. Fairness in LLMs
helps ensure inclusivity, equal representation based on factors such as race,
gender and promotes responsible AI deployment. As the use of LLMs has become
increasingly prevalent, it is essential to assess whether LLMs can generate
fair outcomes when subjected to considerations of fairness. In this study, we
introduce a framework outlining fairness regulations aligned with various
fairness definitions, with each definition being modulated by varying degrees
of abstraction. We explore the configuration for in-context learning and the
procedure for selecting in-context demonstrations using RAG, while
incorporating fairness rules into the process. Experiments conducted with
different LLMs indicate that GPT-4 delivers superior results in terms of both
accuracy and fairness compared to other models. This work is one of the early
attempts to achieve fairness in prediction tasks by utilizing LLMs through
in-context learning.
