A Fairness Taxonomy for the Assessment of Korean Large Language Models

The objective of this study is to define a systematic taxonomy for assessing the fairness of Korean-based large language models (LLMs). To achieve this, various international documents, datasets, and academic papers are collected and analyzed for their presented fairness and bias classification methods. As a result, we propose a classification framework that includes superclasses for fairness assessment and subclasses reflecting the specific characteristics of Korean society. The proposed taxonomy provides a more systematic and detailed basis for assessing the fairness of Korean-based LLMs. This approach is expected to effectively address ethical issues in Korean language models and contribute to the development of AI technology suitable for Korean society.