  Given large language models' (LLMs) increasing integration into workplace
software, it is important to examine how biases in the models may impact
workers. For example, 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 autocomplete suggestions from an
LLM and reported their subsequent perceptions. 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.
