“It’s Complicated”: Negotiating Accessibility and (Mis)Representation in Image Descriptions of Race, Gender, and Disability
Open accessAbstract
Content creators are instructed to write textual descriptions of visual content to make it accessible; yet existing guidelines lack specifics on how to write about people’s appearance, particularly while remaining mindful of consequences of (mis)representation. In this paper, we report on interviews with screen reader users who were also Black, Indigenous, People of Color, Non-binary, and/or Transgender on their current image description practices and preferences, and experiences negotiating theirs and others’ appearances non-visually. We discuss these perspectives, and the ethics of humans and AI describing appearance characteristics that may convey the race, gender, and disabilities of those photographed. In turn, we share considerations for more carefully describing appearance, and contexts in which such information is perceived salient. Finally, we offer tensions and questions for accessibility research to equitably consider politics and ecosystems in which technologies will embed, such as potential risks of human and AI biases amplifying through image descriptions.
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In many tasks, disabled people might have limited access to sensory information that is important to the task, or their desired information might come into tension with other values such as privacy or making racial inference from photos.
“In many tasks, disabled people might have limited access to sensory information that is important to the task [33, 75], or their desired information might come into tension with other values such as privacy or making racial inference from photos [10, 37, 94].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [10]
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References 87 · Cited by 128
- Authors
- Bennett, C. L., Gleason, C., Scheuerman, M. K., Bigham, J. P., Guo, A., & To, A.
- Affiliations
- Carnegie Mellon University (US)
- University of Colorado Boulder (US)
- University of Michigan (US)
- Northeastern University (US)
- ORCID
- Venue
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2021-05-06
- Pages
- 1-19
- OpenAlex
- W3162205072
- Cited by
- 128
- References
- 87
- Citations by year
- 2026: 16 · 2025: 25 · 2024: 24 · 2023: 34 · 2022: 20 · 2021: 9
- FWCI
- 28.57
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Social Sciences › Safety Research › Disability Rights and Representation
- Social Sciences › Arts and Humanities › Language and Linguistics › Subtitles and Audiovisual Media
- Keywords
- picture description, screen readers, visual accessibility, race, gender, disability, misrepresentation, appearance description, non-binary people, transgender people, BIPOC, bias amplification, accessibility guidelines
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