Papers by John Prindle
“You Gotta be a Doctor, Lin” : An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated racial and gender biases in various applications. |
| Approach: | They use Large Language Models to simulate hiring decisions and salary recommendations for candidates with 320 first names that strongly signal their race and gender, across over 750,000 prompts. |
| Outcome: | The proposed models favor candidates with White female-sounding names over other demographic groups across 40 occupations. |
‘Rich Dad, Poor Lad’: How do Large Language Models Contextualize Socioeconomic Factors in College Admission ? (2025.emnlp-main)
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| Challenge: | Large Language Models are increasingly involved in high-stakes domains, yet how they reason about socially sensitive decisions remains underexplored. |
| Approach: | They propose a dual-process audit framework to probe LLMs’ reasoning behaviors in sensitive applications using a synthetic dataset of 30,000 applicant profiles grounded in real-world correlations. |
| Outcome: | The proposed framework exploits a synthetic dataset of 30,000 applicant profiles grounded in real-world correlations to probe LLMs' reasoning behaviors in sensitive applications. |