Given large language models' (LLMs) increasing integration into workplace software, it is important to examine how potential biases they introduce can impact workers. Stylistic biases in the language suggested by LLMs may cause feelings of alienation and result in increased labor for individuals or groups whose style does not match. We examine how such writer-style bias impacts inclusion, control, and ownership over the work when co-writing with LLMs. In an online experiment, participants wrote hypothetical job promotion requests using either hesitant or self-assured auto-complete suggestions from an LLM and reported their subsequent perceptions of inclusion, control, and ownership. We found that the style of the AI model did not impact perceived inclusion. However, individuals with higher perceived inclusion did perceive greater agency and ownership, an effect more strongly impacting participants of minoritized genders. Feelings of inclusion mitigated a loss of control and agency when accepting more AI suggestions.

翻译：鉴于大型语言模型(LLMs)日益融入工作场所软件，研究其可能引入的潜在偏见如何影响工作者至关重要。LLMs建议的语言中的文体偏见可能导致风格不匹配的个体或群体产生疏离感，并增加其劳动强度。我们探讨了在与LLMs协作写作时，此类作者风格偏见如何影响包容性、控制感及作品所有权。在一项在线实验中，参与者使用LLM生成的犹豫型或自信型自动补全建议撰写假设的职位晋升申请，并报告其对包容性、控制感和所有权的感知。研究发现，AI模型的风格并未影响感知包容性。然而，感知包容性较高的个体确实体验到更强的能动性和所有权，该效应在少数性别参与者中影响更为显著。包容感缓解了因采纳更多AI建议而带来的控制感与能动性损失。