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Given large language models' (LLMs) increasing integration into workplacesoftware, it is important to examine how potential biases they introduce canimpact workers. Stylistic biases in the language suggested by LLMs may causefeelings of alienation and result in increased labor for individuals or groupswhose style does not match. We examine how such writer-style bias impactsinclusion, control, and ownership over the work when co-writing with LLMs. Inan online experiment, participants wrote hypothetical job promotion requestsusing either hesitant or self-assured auto-complete suggestions from an LLM andreported their subsequent perceptions of inclusion, control, and ownership. Wefound that the style of the AI model did not impact perceived inclusion.However, individuals with higher perceived inclusion did perceive greateragency and ownership, an effect more strongly impacting participants ofminoritized genders. Feelings of inclusion mitigated a loss of control andagency when accepting more AI suggestions.

The Role of Inclusion, Control, and Ownership in Workplace AI-Mediated Communication