"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations
This work highlights bias risks in LLM-powered employment systems, which could perpetuate discrimination in hiring practices, though it is incremental as it builds on known bias issues in AI.
The study investigated racial and gender bias in large language models (GPT-3.5-Turbo and Llama 3-70B-Instruct) by simulating hiring decisions and salary recommendations for candidates with names signaling race and gender across 40 occupations, finding a preference for White female-sounding names and salary variations of up to 5% between subgroups, with inconsistent alignment to real-world U.S. labor data.
Social science research has shown that candidates with names indicative of certain races or genders often face discrimination in employment practices. Similarly, Large Language Models (LLMs) have demonstrated racial and gender biases in various applications. In this study, we utilize GPT-3.5-Turbo and Llama 3-70B-Instruct 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. Our empirical results indicate a preference among these models for hiring candidates with White female-sounding names over other demographic groups across 40 occupations. Additionally, even among candidates with identical qualifications, salary recommendations vary by as much as 5% between different subgroups. A comparison with real-world labor data reveals inconsistent alignment with U.S. labor market characteristics, underscoring the necessity of risk investigation of LLM-powered systems.