This research introduces a decision framework and toolkit (LangFair) to help practitioners systematically assess and mitigate bias in large language models.

• Maps specific fairness risks to appropriate evaluation metrics
• Provides clear guidance on which bias measures to use for different LLM applications
• Offers a practical implementation approach rather than theoretical discussions
• Enables more responsible AI deployment through use-case specific assessment

For security professionals, this framework creates a structured approach to identifying and addressing potential biases before they manifest as security or ethical vulnerabilities in deployed AI systems.

An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases