Disability Representations: Finding Biases in Automatic Image Generation
This addresses biases in AI-generated imagery that affect people with disabilities, highlighting an incremental need for more inclusive AI development.
The study investigated representation biases in popular text-to-image models towards people with disabilities, finding significant biases such as portrayals as old, sad, and predominantly using manual wheelchairs.
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.