Gender Bias in LLM-generated Interview Responses

2024/10/28 by Haein Kong, Kong, Haein, Ahn, Yongsu +3 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection

paper · pdf · doi:10.48550/arxiv.2410.20739

openalex publication_date 2024/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

LLMs have emerged as a promising tool for assisting individuals in diverse text-generation tasks, including job-related texts. However, LLM-generated answers have been increasingly found to exhibit gender bias. This study evaluates three LLMs (GPT-3.5, GPT-4, Claude) to conduct a multifaceted audit of LLM-generated interview responses across models, question types, and jobs, and their alignment with two gender stereotypes. Our findings reveal that gender bias is consistent, and closely aligned with gender stereotypes and the dominance of jobs. Overall, this study contributes to the systematic examination of gender bias in LLM-generated interview responses, highlighting the need for a mindful approach to mitigate such biases in related applications.

Cited by

• From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship
• Understanding Gender Bias in AI-Generated Product Descriptions

Related

• Assessing Gender Bias in LLMs: Comparing LLM Outputs with Human Perceptions and Official Statistics
• Do LLMs have a Gender (Entropy) Bias?
• Gender and Positional Biases in LLM-Based Hiring Decisions: Evidence from Comparative CV/Résumé Evaluations
• Evaluating LLMs for Gender Disparities in Notable Persons
• Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias