This research introduces Knowledge Graph-Augmented Training (KGAT) as an innovative approach to detect and reduce biases in large language models.

• KGAT leverages structured knowledge to identify and counteract biases present in training data
• Helps prevent models from amplifying existing societal biases
• Enables more responsible and equitable AI deployment across diverse domains
• Addresses critical security concerns for sensitive applications

This work is particularly significant for security professionals as it provides a framework for ensuring AI systems make fair decisions when deployed in high-stakes environments such as hiring, loan approvals, or criminal justice.

Detecting and Mitigating Bias in LLMs through Knowledge Graph-Augmented Training