  Avoiding systemic discrimination requires investigating AI models' potential
to propagate stereotypes resulting from the inherent biases of training
datasets. Our study investigated how text-to-image models unintentionally
perpetuate non-rational beliefs regarding autism. The research protocol
involved generating images based on 53 prompts aimed at visualizing concrete
objects and abstract concepts related to autism across four models: DALL-E,
Stable Diffusion, SDXL, and Midjourney (N=249). Expert assessment of results
was performed via a framework of 10 deductive codes representing common
stereotypes contested by the community regarding their presence and spatial
intensity, quantified on ordinal scales and subject to statistical analysis of
inter-rater reliability and size effects. The models frequently utilised
controversial themes and symbols which were unevenly distributed, however, with
striking homogeneity in terms of skin colour, gender, and age, with autistic
individuals portrayed as engaged in solitary activities, interacting with
objects rather than people, and displaying stereotypical emotional expressions
such as pale, anger, or sad. Secondly we observed representational
insensitivity regarding autism images despite directional prompting aimed at
falsifying the above results. Additionally, DALL-E explicitly denied
perpetuating stereotypes. We interpret this as ANNs mirroring the human
cognitive architecture regarding the discrepancy between background and
reflective knowledge, as justified by our previous research on autism-related
stereotypes in humans.
