Assessing fairness in finetuned scoring models with demographically restricted training data

The increasing adoption of automated essay scoring (AES) in high-stakes educational contexts necessitates careful examination of potential biases within the systems. This study investigates how the demographic composition of training data influences fairness in AES systems developed from finetuned large language models (LLMs). Using the PERSUADE corpus of 26,000 student essays, we conducted a systematic analysis using demographically restricted training sets to isolate the impact of training dat