Dissecting Bias of ChatGPT in College Major Recommendations
This addresses bias in AI recommendations for education, which is an incremental analysis of existing models.
The study investigated bias in ChatGPT's college major recommendations for high school students, finding significant disparities based on demographic and educational factors using metrics like Jaccard Coefficient and STEM Disparity Score.
I investigate bias in terms of ChatGPT's college major recommendations for students with various profiles, looking at demographic disparities in factors such as race, gender, and socioeconomic status, as well as educational disparities such as score percentiles. By constructing prompts for the ChatGPT API, allowing the model to recommend majors based on high school student profiles, I evaluate bias using various metrics, including the Jaccard Coefficient, Wasserstein Metric, and STEM Disparity Score. The results of this study reveal a significant disparity in the set of recommended college majors, irrespective of the bias metric applied.