Kelly Avery Mack
University of Washington, Seattle, Washington, United States
This paper reports on disability representation in images output from text-to-image (T2I) generative AI systems. Through eight focus groups with 25 people with disabilities, we found that models repeatedly presented reductive archetypes for different disabilities. Often these representations reflected broader societal stereotypes and biases, which our participants were concerned to see reproduced through T2I. Our participants discussed further challenges with using these models including the current reliance on prompt engineering to reach satisfactorily diverse results. Finally, they offered suggestions for how to improve disability representation with solutions like showing multiple, heterogeneous images for a single prompt and including the prompt with images generated. Our discussion reflects on tensions and tradeoffs we found among the diverse perspectives shared to inform future research on representation-oriented generative AI system evaluation metrics and development processes.
doi.org/10.1145/3613904.3642166
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)