REVIEW 4 cited by
"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
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.
Forward citations
Cited by 4 Pith papers
-
Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages
Across nine Asian languages, multilingual LLMs favor Western cultural entities in 30-40% of culturally grounded contexts, with model-specific sentiment biases and extraction accuracy gaps.
-
AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
LLMs that screen resumes systematically prefer their own generated summaries over human-written ones, with simulated shortlisting advantages of 23 to 60 percent for same-model users.
-
On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena
Arab cultural entities that double as everyday Arabic words are harder for language models to recognize, especially when tokenized as single tokens.
-
"All that Glitters": Approaches to Evaluations with Unreliable Model and Human Annotations
Encoder models trained on noisy classroom ratings look super-human under standard concordance metrics, but generalizability, disattenuation, and hierarchical rater analyses show the apparent advantage is partly spurio...
Discussion (0). Continue with ORCID to comment.