BEATS introduces a robust evaluation framework for measuring bias, ethics, fairness, and factuality in Large Language Models across 29 distinct metrics.

• Covers demographic, cognitive, and social biases in LLMs
• Assesses ethical reasoning capabilities and group fairness
• Evaluates factuality and misinformation risks
• Provides a standardized approach to identify AI system vulnerabilities

This research is critical for security professionals as it helps identify potential biases that could lead to discriminatory outcomes when LLMs are deployed in decision-making systems. By systematically measuring these biases, organizations can implement appropriate safeguards before deployment.

BEATS: Bias Evaluation and Assessment Test Suite for Large Language Models