English

Disability Representations: Finding Biases in Automatic Image Generation

Computer Vision and Pattern Recognition 2024-06-24 v1 Computation and Language

Abstract

Recent advancements in image generation technology have enabled widespread access to AI-generated imagery, prominently used in advertising, entertainment, and progressively in every form of visual content. However, these technologies often perpetuate societal biases. This study investigates the representation biases in popular image generation models towards people with disabilities (PWD). Through a comprehensive experiment involving several popular text-to-image models, we analyzed the depiction of disability. The results indicate a significant bias, with most generated images portraying disabled individuals as old, sad, and predominantly using manual wheelchairs. These findings highlight the urgent need for more inclusive AI development, ensuring diverse and accurate representation of PWD in generated images. This research underscores the importance of addressing and mitigating biases in AI models to foster equitable and realistic representations.

Keywords

Cite

@article{arxiv.2406.14993,
  title  = {Disability Representations: Finding Biases in Automatic Image Generation},
  author = {Yannis Tevissen},
  journal= {arXiv preprint arXiv:2406.14993},
  year   = {2024}
}

Comments

Presented at AVA Workshop of CVPR 2024

R2 v1 2026-06-28T17:14:30.272Z