Gender Stereotypes toward Non-gendered Generative AI: The Role of Gendered Expertise and Gendered Linguistic Cues

With rapid advancements in large language models (LLMs), generative AI (GenAI) is transforming people's life and work across various domains. Unlike previous AI technologies that are often feminized, most of these GenAI tools are non-gendered, potentially preventing users from applying gender stereotypes. However, GenAI's use of natural language can evoke social perceptions including gender attribution, making it susceptible to gender associations. Using two online experiments, we explored how GenAI's removal of gender could mitigate individuals' gender stereotypes toward it, and how certain linguistic cues could trigger gender stereotypes even if it is non-gendered. We found that the removal of AI's gender did mitigate gender stereotypes toward it, but only to an extent. Additionally, gendered linguistic cues such as politeness, apologies, and tentative language (or lack thereof) could trigger gender stereotypes toward non-gendered AI. We contribute to HCI and CSCW research by providing a timely investigation into individuals' gender stereotypes toward GenAI agents, and one of the first to examine the profound impact of language style on users' perceptions and attributions of social characteristics to GenAI. Our findings shed light on the purposeful and responsible design of GenAI that prioritizes promoting gender equality, thereby ensuring that technological advancements align with evolving social values.