  The rapid evolution of Large Language Models (LLMs) highlights the necessity
for ethical considerations and data integrity in AI development, particularly
emphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable)
data principles. While these principles are crucial for ethical data
stewardship, their specific application in the context of LLM training data
remains an under-explored area. This research gap is the focus of our study,
which begins with an examination of existing literature to underline the
importance of FAIR principles in managing data for LLM training. Building upon
this, we propose a novel framework designed to integrate FAIR principles into
the LLM development lifecycle. A contribution of our work is the development of
a comprehensive checklist intended to guide researchers and developers in
applying FAIR data principles consistently across the model development
process. The utility and effectiveness of our framework are validated through a
case study on creating a FAIR-compliant dataset aimed at detecting and
mitigating biases in LLMs. We present this framework to the community as a tool
to foster the creation of technologically advanced, ethically grounded, and
socially responsible AI models.
