Solar is a novel fairness framework for evaluating and addressing social biases embedded in code automatically generated by Large Language Models.

• Systematically identifies hidden biases in LLM-generated code through automated test case generation
• Quantitatively measures bias presence across different demographic groups
• Proposes effective mitigation strategies to reduce discriminatory outcomes
• Addresses a critical security gap in AI-assisted software engineering

This research is essential for security professionals as biased code can introduce vulnerabilities and discriminatory outcomes in applications, leading to potential legal and ethical issues. Implementing fairness frameworks like Solar helps ensure AI-generated code meets equitable standards across diverse user populations.

Bias Unveiled: Investigating Social Bias in LLM-Generated Code