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

Large language models (LLMs) can exhibit social biases. Given LLMs' increasing integration into workplace software, these biases may impact workers' well-being and may disproportionately impact minoritized communities. This short paper investigates how co-writing with an LLM impacts three measures related to user's well-being: feelings of inclusion, control, and ownership over their work. In an online experiment, participants wrote hypothetical job promotion requests to their boss and using either hesitant or self-assured auto-complete suggestions from an LLM. Afterward, participants reported their feelings of inclusion, control, and ownership. We found that the style of the AI model did not impact perceived inclusion. Furthermore, individuals with higher perceived inclusion also perceived greater agency and ownership, an effect more strongly impacting participants of minoritized genders. Lastly, feelings of inclusion can mitigate a loss of control and agency when accepting more AI suggestions. Future work should explore feelings of inclusion in AI-written communication.