  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.
