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

TL;DR

This study investigates how stylistic biases in large language models affect perceptions of inclusion, control, and ownership in workplace communication, revealing that perceived inclusion influences feelings of agency and mitigates control loss.

Contribution

It provides empirical evidence on the impact of AI stylistic biases on workplace perceptions, highlighting the importance of inclusion in AI-mediated communication.

Findings

AI style did not affect perceived inclusion.

Higher perceived inclusion increased feelings of agency and ownership.

Inclusion mitigated perceived loss of control when accepting AI suggestions.

Abstract

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.