"Before, I Asked My Mom, Now I Ask ChatGPT": Visual Privacy Management with Generative AI for Blind and Low-Vision People
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Abstract (arXiv)
Blind and low vision (BLV) individuals use Generative AI (GenAI) tools to interpret and manage visual content in their daily lives. While such tools can enhance the accessibility of visual content and so enable greater user independence, they also introduce complex challenges around visual privacy. In this paper, we investigate the current practices and future design preferences of blind and low vision individuals through an interview study with 21 participants. Our findings reveal a range of current practices with GenAI that balance privacy, efficiency, and emotional agency, with users accounting for privacy risks across six key scenarios, such as self-presentation, indoor/outdoor spatial privacy, social sharing, and handling professional content. Our findings reveal design preferences, including on-device processing, zero-retention guarantees, sensitive content redaction, privacy-aware appearance indicators, and multimodal tactile mirrored interaction methods. We conclude with actionable design recommendations to support user-centered visual privacy through GenAI, expanding the notion of privacy and responsible handling of others data.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 25 of its 95 references that are in the map. First posted on arXiv 30 Jun 2025.
Builds on
25 of 95 references.
References · 25
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2022Open-Vocabulary Semantic Segmentation with Mask-adapted CLIPcited by 565
- 2022Expanding Language-Image Pretrained Models for General Video Recognitioncited by 321
- 2021ActionCLIP: A New Paradigm for Video Action Recognitioncited by 320
- 2022Fine-tuned CLIP Models are Efficient Video Learnerscited by 179
- 2022Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detectioncited by 146
- 2023Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programmingcited by 89
- 2023Audio-Visual LLM for Video Understandingcited by 77
- 2024LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editingcited by 47
- 2015Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sourcescited by 42
- 2024CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applicationscited by 33
- 2024WorldScribe: Towards Context-Aware Live Visual Descriptionscited by 27
13 more
- 2024Investigating Use Cases of AI-Powered Scene Description Applications for Blind and Low Vision Peoplecited by 24
- 2023Designing Participatory AI: Creative Professionals' Worries and Expectations about Generative AIcited by 20
- 2023GenAssist: Making Image Generation Accessiblecited by 18
- 2025Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Modelscited by 13
- 2025Towards Understanding the Use of MLLM-Enabled Applications for Visual Interpretation by Blind and Low Vision Peoplecited by 11
- 2024Empowering Users in Digital Privacy Management through Interactive LLM-Based Agentscited by 9
- 2022"It Feels Like Taking a Gamble": Exploring Perceptions, Practices, and Challenges of Using Makeup and Cosmetics for People with Visual Impairmentscited by 7
- 2025The Impact of Generative AI Coding Assistants on Developers Who Are Visually Impairedcited by 7
- 2024Adanonymizer: Interactively Navigating and Balancing the Duality of Privacy and Output Performance in Human-LLM Interactioncited by 6
- 2024How Can Generative AI Enhance the Well-being of Blind?cited by 5
- 2023Understanding How to Inform Blind and Low-Vision Users about Data Privacy through Privacy Question Answering Assistantscited by 3
- 2024Exploring Use and Perceptions of Generative AI Art Tools by Blind Artistscited by 2
- 2025Raising Awareness of Location Information Vulnerabilities in Social Media Photos using LLMscited by 2
Built on by
Citing papers per year: 2026: 6.
6 papers.
- 2026How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision Peoplecited by 3
- 2026Finding the Signal in the Noise: An Exploratory Study on Assessing the Effectiveness of AI and Accessibility Forums for Blind Users' Support Needscited by 1
- 2026Bespoke Visual Assistance: What and How do Blind and Low-Vision People Create with Agentic Programming?cited by 0
- 2026Chart-Supported or Model-Supplied? Examining MLLM-Generated Claims for Accessible Visualizationcited by 0
- 2026Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documentscited by 0
- 2026More Than Just Access: Generative AI as Communication Intermediary for Blind and Low-Vision Userscited by 0
- 2026More Than Just Access: Generative AI as Communication Intermediary for Blind and Low-Vision Users14 Sep 2026
- 2026Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documents26 Aug 2026
- 2026Chart-Supported or Model-Supplied? Examining MLLM-Generated Claims for Accessible Visualization27 Jul 2026
- 2026Bespoke Visual Assistance: What and How do Blind and Low-Vision People Create with Agentic Programming?23 Jul 2026
- 2026Finding the Signal in the Noise: An Exploratory Study on Assessing the Effectiveness of AI and Accessibility Forums for Blind Users' Support Needs20 Feb 2026
- 2026How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People13 Feb 2026