This research introduces a comprehensive detection framework for identifying subtle biases in LLMs that could propagate misinformation or reinforce stereotypes.

• Integrates contextual analysis to capture nuanced biases not detected by conventional methods
• Focuses on enhancing model transparency to enable responsible LLM deployment
• Addresses biases across multiple domains including security, education, legal, and medical contexts
• Offers practical solutions for ethical AI development and implementation

For security professionals, this framework provides essential tools to evaluate AI systems for hidden biases that could compromise decision-making integrity, create security vulnerabilities, or introduce ethical risks in deployed applications.

Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases