Social Bias in LLM-Generated Code: Benchmark and Mitigation
Large Language Models (LLMs) are increasingly deployed to generate code for human-centered applications where demographic fairness is critical. However, existing evaluations focus almost exclusively on functional correctness, leaving social bias in LLM-generated code largely unexamined. Extending our prior work on Solar, we conduct a comprehensive empirical study using SocialBias-Bench, a benchmark of 343 real-world coding tasks spanning seven demographic dimensions. We evaluate four prominent LLMs and find severe bias across all models, with Code Bias Scores reaching up to 60.58%. We further show that standard prompt-level interventions, such as Chain-of-Thought reasoning and fairness pe...
| Authors | Fazle Rabbi, Lin Ling, Song Wang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 1 May 2026 |
| Last updated at source | 5 May 2026 |
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