BiasCause: Evaluate Socially Biased Causal Reasoning of Large Language Models

ACL ARR 2025 May Submission3 Authors

Abstract: While large language models (LLMs) already play significant roles in the society, research has shown that LLMs still generate content including social bias against certain sensitive groups. While existing benchmarks have effectively identified social biases in LLMs, a critical gap remains in our understanding of the underlying reasoning that lead to these biased outputs. This paper goes one step further to evaluate the causal reasoning process of LLMs when they answer questions eliciting social biases. We first propose a novel conceptual framework to classify the causal reasoning produced by LLMs. Next, we use LLMs to synthesize $1788$ questions covering $8$ sensitive attributes and manually validate them. The questions can test different kinds of causal reasoning by letting LLMs disclose their reasoning process with causal graphs. We then test 4 state-of-the-art LLMs. All models answer majority of questions with biased causal reasoning, resulting in a total of $4135$ biased causal graphs. Meanwhile, we discover $3$ strategies for LLMs to avoid biased causal reasoning by analyzing the "bias-free" cases. Finally, we reveal that LLMs are also prone to "mistaken-biased" causal reasoning, where they first confuse correlation with causality to infer specific sensitive group names and then incorporate a biased causal reasoning.

Paper Type: Long

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: model bias evaluation, model bias understanding

Contribution Types: Data resources, Data analysis

Languages Studied: English

Keywords: Biased causal reasoning of LLMs

Submission Number: 3