“Trying to Piece It Together”: Exploring Accessible Error Detection in Emerging Privacy Techniques With Blind People
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Blind people use visual assistance technologies (VAT) to access visual information, yet VAT can expose blind people to privacy risks.Prior HCI research has studied and built AI-enabled obfuscation techniques to detect and remove private content.However, blind people cannot easily spot errors in obfuscation tools.Our paper explores how assessment descriptors, brief visual attributes of objects, may enable blind people to find errors.By conducting interviews and focus groups with blind participants, we found that certain assessment descriptors (color, dimensions, distance) are inadequate to support blind people.Instead, participants discussed assessment descriptors that better reflect their sensemaking process, such as describing multiple objects in a particular space.Expanding the scope of accessible verification beyond assessment descriptors, participants called for greater transparency on how AI-enabled privacy techniques are developed and emphasized the need to co-create training materials on using AI-enabled privacy techniques.Building from our findings and disability studies scholarship, our paper examines how sighted bias could produce assessment descriptors that neglect the needs of blind people and analyzes how participants' preferred assessment descriptors contrast with existing standards of visual description.Lastly, we offer design directions to push for greater transparency in VAT and obfuscation tools.
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Both general-purpose and blind-focused AI systems often miss information crucial to BLV users or assume sighted knowledge when generating visual descriptions.
“Research shows that both general-purpose and blind-focused AI systems often miss information crucial to BLV users (e.g., nutrition labels on products [66]) or assume sighted knowledge when generating visual descriptions [4].”
From Cripping AI: Reimagining AI Through Lived Disability Experiences (2026) · cites this paper as [4]
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Cripping AI: Reimagining AI Through Lived Disability Experiences
Tang, X., Lin, T.-A., Li, J., & Wu, S. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3806744 · Cited by 2 · see citing works
- Authors
- Alharbi, R., Cheong, A. D., Herskovitz, J., Brewer, R. N., & Schoenebeck, S.
- Affiliations
- University of Michigan (US)
- School of Information, University of Michigan, Ann Arbor, Michigan, USA
- Venue
- Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility
- Publisher
- ACM
- Published
- 2025-10-22
- Pages
- 1-20
- OpenAlex
- W4415444975
- Cited by
- 3
- References
- 18
- Citations by year
- 2026: 3
- FWCI
- 3.68
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) (publishedVersion, cc-by)
- Funders
- National Science Foundation (1763297)
- Google Research Inclusion Award
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- blind people, privacy risks, error detection, assessment descriptors, visual description, transparency, accessibility, disability studies, sensemaking, co-design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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