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Cited by 15Assistive Technology in Communication and MobilityPrivacy, Security, and Data ProtectionDigital Accessibility for Disabilities

Dump It, Destroy It, Send It to Data Heaven': Blind People's Expectations for Visual Privacy in Visual Assistance Technologies

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Stangl, Abigale, Sadjo, Emma, Emami-Naeini, Pardis, Wang, Yang, Gurari, Danna, Findlater, Leah·2023·20th International Web for All Conference
DOI 10.1145/3587281.3587296
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Abstract

Visual assistance technologies provide people who are blind with access to information about their visual surroundings by digitally connecting them to remote humans or artificial intelligence systems that describe visual content such as objects, people, scenes, and text observed in their live image/video feeds. Prior work has revealed that users have concerns about how such technologies handle private visual content captured in their image/video feeds. Yet, it remains unclear how users want technologies to manage such private content. To fill this gap, we interviewed 16 totally blind individuals to learn about their expectations for visual privacy when using visual assistance technologies. Our findings reveal three overarching user-centered expectations associated with visual privacy-preservation in this domain, as well as the broader ethical challenges involved with developing AI-based privacy-preserving visual assistance technologies.

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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 [114]

    Section 4.3.2 (page unavailable) reports privacy concerns about collecting and storing blind participants’ visual data in AI-based visual assistance technologies. The paper does not establish that participants expressed those concerns when using LLMs; that is a more specific technology than the one studied. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Stangl, A., Sadjo, E., Emami-Naeini, P., Wang, Y., Gurari, D., & Findlater, L.
Affiliations
  • University of Washington (US)
  • Duke University (US)
  • University of Illinois Urbana-Champaign (US)
  • University of Colorado Boulder (US)
ORCID
Venue
20th International Web for All Conference
Publisher
ACM
Published
2023-04-30
Pages
134-147
DOI
10.1145/3587281.3587296
OpenAlex
W4366762612
Cited by
15
References
19
Citations by year
2026: 3 · 2025: 4 · 2024: 7 · 2023: 1
FWCI
10.39
Type
conference paper
Language
en
Open access
Licence
Locations
International Web for All Conference (W4A) (publishedVersion, other-oa)
Funders
National Science Foundation (2148080, CRA Computing Innovation Fellows)
Topics
  • Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
Keywords
visual privacy, blind users, user expectations, privacy preservation, ethical considerations, assistive technology, data management

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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.

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