Toward Community- Led Evaluations of Text-to-Image AI Representations of Disability, Health, and Accessibility

Responsible AI advocates for user evaluations, particularly when concerning people with disabilities, health conditions, and accessibility needs ( DHA)–wide- ranging but umbrellaed sociodemograph- ics. However, community- centered text- to- image AI’s ( T2I) evaluations are often researcher- led, situating evaluators as consumers. We instead recruited 21 people with diverse DHA to evaluate T2I by writing and editing their own T2I prompts with their preferred language and topics, in a method mirroring everyday use. We contribute user- generated terminology categories which inform future research and data collections, necessary for developing authentic scaled evaluations. We additionally surface yet- discussed DHA AI harms intersecting race and class, and participants shared harm impacts they experienced as image- creator evaluators. To this end, we demonstrate that prompt engineering– proposed as a misrepresentation mitigation– was largely ineffective at improving DHA representations. We discuss the importance of evaluator agency to increase ecological validity in community- centered evaluations, and opportunities to research iterative prompting as an evaluation technique.