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Cited by 35Privacy, Security, and Data ProtectionAssistive Technology in Communication and MobilityDigital Accessibility for Disabilities

Privacy Concerns for Visual Assistance Technologies

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Stangl, Abigale, Shiroma, Kristina, Davis, Nathan, Xie, Bo, Fleischmann, Kenneth R., Findlater, Leah, Gurari, Danna·2022·ACM Transactions on Accessible Computing
DOI 10.1145/3517384
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Abstract

People who are blind share their images and videos with companies that provide visual assistance technologies (VATs) to gain access to information about their surroundings. A challenge is that people who are blind cannot independently validate the content of the images and videos before they share them, and their visual data commonly contains private content. We examine privacy concerns for blind people who share personal visual data with VAT companies that provide descriptions authored by humans or artifcial intelligence (AI) . We frst interviewed 18 people who are blind about their perceptions of privacy when using both types of VATs. Then we asked the participants to rate 21 types of image content according to their level of privacy concern if the information was shared knowingly versus unknowingly with human- or AI-powered VATs. Finally, we analyzed what information VAT companies communicate to users about their collection and processing of users’ personal visual data through their privacy policies. Our fndings have implications for the development of VATs that safeguard blind users’ visual privacy, and our methods may be useful for other camera-based technology companies and their users.

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  • BLV people in our survey and prior work expressed concerns about data privacy when using LLMs.

    “BLV people in our survey and prior work [114, 115] expressed concerns about data privacy when using LLMs”

    From "It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models (2026) · cites this paper as [115]

    The 3.3 Summary and “AI-powered VAT-Risks” passages (page unavailable) report blind participants’ concerns about how AI-powered visual assistance technologies collect, store, and handle their data. They do not establish concerns about data privacy when using LLMs specifically. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Authors
Stangl, A., Shiroma, K., Davis, N., Xie, B., Fleischmann, K. R., Findlater, L., & Gurari, D.
Affiliations
  • University of Washington (US)
  • The University of Texas at Austin (US)
  • University of Colorado Boulder (US)
ORCID
Venue
ACM Transactions on Accessible Computing
Publisher
Association for Computing Machinery (ACM)
Published
2022-05-19
Volume
15
Issue
2
Pages
1-43
ISSN
  • 1936-7228 (ISSN-L)
  • 1936-7236
DOI
10.1145/3517384
OpenAlex
W4213427560
Cited by
35
References
8
Citations by year
2026: 8 · 2025: 10 · 2024: 12 · 2023: 4 · 2022: 1
FWCI
9.71
Type
journal article
Language
en
Open access
Licence
Locations
ACM Transactions on Accessible Computing (publishedVersion, cc-by)
Funders
  • University of Texas at Austin Good Systems Grand Challenge
  • National Science Foundation/Computing Research Association 2020 Computing Innovation Fellows
  • University of Washington Center for Research and Education on Accessible Technology and Experiences
  • National Science Foundation (SaTC-2126314)
Topics
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
  • Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
Keywords
visual privacy, blind users, image sharing, privacy concerns, privacy policies

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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