@inproceedings{Gaba26facct,
  author = {Aimen Gaba and Emily Wall and Os Keyes and Kyle Wm Hall and Yuriy Brun and Cindy Xiong Bearfield},
  title =
  {A Gender Diverse Perspective of Bias in Large Language Models},
  booktitle = {Proceedings of the 9th ACM Conference on Fairness, Accountability, and Transparency (FAccT)},
  venue = {FAccT},
  address = {Montreal, QC, Canada},
  month = {June},
  date = {25--28},
  year = {2026},

  abstract = {Concerns about bias in large language models (LLMs) often
  focus on technical metrics, yet limited research examines how marginalized
  communities perceive and interpret model behavior. Through 25 in-depth
  interviews with participants across gender identities (non-binary, men, and
  women), we investigate how ChatGPT responds to gendered versus neutral
  prompts and how users evaluate bias in these responses. Our findings reveal
  striking differences: non-binary participants identified condescension,
  stereotyping, and identity erasure in outputs that cisgender participants
  often found acceptable, demonstrating that bias perception is not
  universal, but rather shaped by lived experience and social positioning.
  This differential perception reveals a fundamental limitation in current
  fairness evaluation: technical metrics and aggregate user feedback
  systematically miss harms that are visible primarily to marginalized users.
  Our work demonstrates that evaluating LLM fairness requires centering the
  perspectives of those most affected by algorithmic bias, rather than
  relying solely on technical detection or treating all user feedback as
  equivalent, and makes recommendations for how to go about this.}
}