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Explore a 15-minute conference talk that examines how text-to-image AI systems represent disability, health, and accessibility through a community-centered evaluation framework. Learn about research methodologies that prioritize disabled community perspectives in assessing AI-generated visual content, moving beyond traditional academic evaluation approaches. Discover insights from authors Cynthia L. Bennett, Shaun K. Kane, and Christina Harrington as they present findings on the gaps between AI representations and authentic disability experiences. Understand the importance of community-led research in identifying biases and stereotypes in AI-generated imagery related to disability and health. Gain knowledge about inclusive AI development practices that center marginalized voices in the evaluation process, and examine how current text-to-image models may perpetuate harmful representations of disability and accessibility needs.
