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• Misfitting With AI: How Blind People Verify and Contest AI Errors

  Rahaf Alharbi, Pa Lor, Jaylin Herskovitz, Sarita Schoenebeck, Robin N. Brewer · 2024 · ASSETS '24: Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility

  This paper presents an in-depth qualitative study with 26 blind participants examining how they encounter, verify, and contest errors produced by AI-enabled visual assistance technologies (AI VAT) such as Seeing AI, Be My Eyes, and ChatGPT. While blind people increasingly rely…

  blind users · artificial intelligence · visual assistance technology · explainable AI · AI errors

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

  Abigale Stangl, Emma Sadjo, Pardis Emami-Naeini, Yang Wang, Danna Gurari, Leah Findlater · 2023 · Proceedings of the 20th International Web for All Conference (W4A)

  This paper investigates the visual privacy expectations of 16 totally blind individuals who use visual assistance technologies (VATs) such as Aira, Be My Eyes, Seeing AI, and Envision AI. VATs provide blind users access to visual information by connecting them to remote human…

  visual privacy · blind users · visual assistance technology · privacy expectations · data protection

• Privacy Concerns for Visual Assistance Technologies

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

  This comprehensive study examines privacy concerns of blind users when sharing images and videos with visual assistance technologies (VATs) like Aira, Be My Eyes, and Seeing AI. The research addresses a fundamental tension: blind people must share visual data to receive…

  visual impairment · privacy · visual assistance technology · artificial intelligence · remote sighted assistance

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