BiasCause introduces a novel framework to evaluate not just what biases exist in LLMs, but why they occur by analyzing the causal reasoning process behind biased outputs.

• Moves beyond identifying bias to understanding the reasoning mechanisms that produce bias
• Evaluates how LLMs form causal connections between social attributes and outcomes
• Provides deeper insights into why models generate socially biased content
• Enables more targeted approaches to mitigating harmful biases

For security professionals, this research is critical as it helps identify fundamental reasoning flaws in AI systems that could lead to discriminatory outcomes when deployed in sensitive decision-making contexts.

BiasCause: Evaluate Socially Biased Causal Reasoning of Large Language Models