mlsanityData to 7 Oct 2026

Dissecting Bias of ChatGPT in College Major Recommendations

arXiv 2401.11699 ↗First posted 18 Dec 2023cs.CY cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 2 of its 17 references that are in the map. First posted on arXiv 18 Dec 2023.

Builds on

2 of 17 references.

References · 2

  1. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 294
  2. 2018Bias Disparity in Recommendation Systemscited by 12

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Educationcited by 9
  2. 2025Assessing Agentic Large Language Models in Multilingual National Biascited by 0
  3. 2025Generative AI in Heritage Practice: Improving the Accessibility of Heritage Guidancecited by 0