When Who You Are Can Change the Code You Get: A Study of Persona-Induced Bias in LLM Code Generation
Large Language Models (LLMs) are widely used as programming assistants, yet it remains unclear whether and how user's demographic information impacts the technical quality of generated code. We conduct a large-scale empirical study of persona-induced bias in LLM-based code generation, focusing a proprietary model (Gemini 2.5 Pro) and an open-weight model (GPT-OSS-120B). Using 18 demographic personas spanning nationality, gender, and experience level, we compare persona-induced prompts against a neutral baseline. Across 35,000+ generated programs, we analyze demographic marker leakage in reasoning and responses, as well as differences in functional correctness, maintainability, code style,...
| Authors | Anubhav Gupta, Mayara Costa Figueiredo, Leticia Santos Machado et al. |
|---|---|
| Primary category | cs.SE |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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