Potential biases and fairness in LLM-based data imputation for sociological research

This paper explores the risks of bias and fairness issues when using large language models to impute missing data in sociological surveys. Drawing on recent research, it highlights how LLMs can reproduce stereotypes and underrepresent minority groups. The article highlights the importance of fairness-aware approaches to ensure ethical and accurate use of LLMs in social science research.