A Framework for Identifying, Categorizing, and Explaining Bias in AI-Generated Code

The paper presents a taxonomy-driven framework for identifying, categorizing, and explaining bias in AI-generated Python code. By extending an existing dataset and manually annotating bias categories and justifications, the authors evaluate both proprietary and open-source large language models (LLMs) for automated bias detection and explanation. Results show that models such as Gemini and Qwen3-coder achieve high classification accuracy and produce justification and code identification similarities that closely match human-authored reasoning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• safety