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.

翻译：前沿图像生成技术因生成高质量图像而受到赞誉，预示着其在多种应用场景中具有普遍前景。然而，初步研究已指出预测偏差可能造成的潜在危害——这种偏差不仅反映文化刻板印象，还可能在现实中强化这些刻板印象。本研究首次探究多模态模型如何处理多元性别身份。具体而言，我们通过系统分析，对比了三个图像生成模型在包含顺性别与非顺性别身份术语的提示下输出的结果。研究发现，某些非顺性别身份被持续（错误）呈现为“更不具人性化”、“更具刻板印象”和“更被色情化”。我们在实验分析之外补充了（a）针对非顺性别个体的问卷调查和（b）系列访谈，以明确受影响个体预期可能遭受的伤害及其期望的呈现方式。受访者尤其担忧错误呈现会助长有害行为与信念。对于简单粗暴的过滤策略（如限制冒犯性内容），受访者普遍持反对态度；反之，他们呼吁社区参与、精选训练数据及个性化定制能力。这些改进有望开辟一条由受影响社区主导变革的未来路径，使技术能够积极“以我们甚至未曾想象的方式呈现酷儿身份”，而非复制陈旧、冒犯性的刻板印象。