Stereotypes and Smut: The (Mis)representation of Non-cisgender
  Identities by Text-to-Image Models
Eddie L. Ungless Björn Ross Anne Lauscher
Cutting-edge image generation has been praised for producing high-quality
images, suggesting a ubiquitous future in a variety of applications. However,
initial studies have pointed to the potential for harm due to predictive bias,
reflecting and potentially reinforcing cultural stereotypes. In this work, we
are the first to investigate how multimodal models handle diverse gender
identities. Concretely, we conduct a thorough analysis in which we compare the
output of three image generation models for prompts containing cisgender vs.
non-cisgender identity terms. Our findings demonstrate that certain
non-cisgender identities are consistently (mis)represented as less human, more
stereotyped and more sexualised. We complement our experimental analysis with
(a)~a survey among non-cisgender individuals and (b) a series of interviews, to
establish which harms affected individuals anticipate, and how they would like
to be represented. We find respondents are particularly concerned about
misrepresentation, and the potential to drive harmful behaviours and beliefs.
Simple heuristics to limit offensive content are widely rejected, and instead
respondents call for community involvement, curated training data and the
ability to customise. These improvements could pave the way for a future where
change is led by the affected community, and technology is used to positively
``[portray] queerness in ways that we haven't even thought of'' rather than
reproducing stale, offensive stereotypes.