Is Your Instruction Treated Fairly? Uncovering and Mitigating Expression Style-Induced Bias in LLM Agents

With the growing deployment of large language model-based agents (LLM agents), there is a heightened need to examine their fairness across diverse users. While prior research on fairness and bias has mainly centered on demographic attributes (e.g., gender and race), we identify a novel and practically significant source of disparity: the **expression style of user instructions**. Unlike task-relevant features such as instruction structure and information completeness, both of which are expected to affect task performance, expression style is inherently task-content-irrelevant and thus should not lead to performance disparities in an LLM agent. To systematically study this underexplored bias, we introduce the *Politeness-Arousal Style Framework* ($\text{PA-Style}$), a two-dimensional taxonomy that characterizes user instructions along polite versus direct and calm versus emotional axes, yielding four distinct compound expression styles. Using PA-Style, we extensively evaluate various LLM agents, revealing substantial performance gaps across expression style conditions. To mitigate these disparities without sacrificing task success rate, we propose *Instruction Style Alignment* ($\text{ISA}$), a lightweight and controllable approach that converts user instructions to a unified expression style. Through extensive experiments across diverse settings, we show that ISA effectively eliminates expression style-induced performance disparities while preserving task performance. Our code is available at [codes](https://anonymous.4open.science/r/isa_).