A Framework for Identifying, Categorizing, and Explaining Bias in AI-Generated Code
As Large Language Models (LLMs) become integrated into software development workflows, concerns regarding unintentional biases in AI-generated code. Although evidence suggests these biases exist, limited research has systematically identified, categorized, and explained them. This study investigates bias in AI-generated code and evaluates whether LLMs can reliably identify and explain it through a taxonomy-driven framework. We extended an existing dataset of biased AI-generated Python code and manually annotated snippets with bias categories and human-authored justifications to establish a ground-truth dataset. Using this dataset, we evaluated proprietary and open-source LLMs as automated...
| Authors | Manaal Basha, Aimee M. Ribeiro, Gema Rodriguez-Perez |
|---|---|
| Primary category | cs.SE |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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