Large language models (LLMs) are the new hot trend being rapidly integrated into products and services—often, in chatbots. LLM-powered chatbots are expected to respond to any number of topics, including topics central to gender identity. In light of rising anti-trans discourse, we examined how two popular LLMs responded to real-world English-language questions about trans identity taken from Quora. We employed reflexive analysis that centered our situated knowledges of the trans community. We found that LLMs return pro-trans responses, even when presented with highly transphobic user prompts. While we also found highly transphobic LLM responses, we found that anti-trans sentiment in LLMs was often subtle, requiring a deep positional understanding from diverse trans stakeholders to interpret. Based on these findings, we recommend diverging from current “value-neutral” approaches that validate transphobia by taking an “all sides” approach. We provide considerations for both the evaluation and design of LLMs that center positional expertise.
Citation
Morgan Scheuerman, Katy Weathington, Adrian Petterson, Dylan Thomas Doyle, Dipto Das, Michael Ann DeVito, and Jed R. Brubaker. 2025. Transphobia is in the Eye of the Prompter: Trans-Centered Perspectives on Large Language Models. ACM Trans. Comput.-Hum. Interact. Just Accepted (June 2025). https://doi.org/10.1145/3743676