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.

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