Dissecting Bias of ChatGPT in College Major Recommendations
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913203085115392 |
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| author | Zheng, Alex |
| author_facet | Zheng, Alex |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11699 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dissecting Bias of ChatGPT in College Major Recommendations Zheng, Alex Computers and Society Artificial Intelligence 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. |
| title | Dissecting Bias of ChatGPT in College Major Recommendations |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2401.11699 |