Paragraph 1:
In today’s digitized world, visual information pervades nearly every aspect of daily life—from reading medication labels to interpreting financial statements. For blind and low-vision individuals, accessing and managing such visual content often requires relying on human assistance, such as family members or friends. While these support systems are indispensable, they inevitably raise concerns about privacy, autonomy, and dependency. How can individuals maintain control over sensitive visual information without feeling intrusive or burdensome to others? Recent advances in generative artificial intelligence (AI) offer a transformative solution, enabling autonomous interpretation of visual data while preserving privacy. This shift, encapsulated in the phrase “I used to ask my mom, now I ask ChatGPT,” reflects a broader cultural and technological transition: human assistance is increasingly supplemented or replaced by AI systems capable of providing context-sensitive, personalized, and secure guidance.
Paragraph 2:
Generative AI models, especially those integrating computer vision and natural language processing, provide novel opportunities to bridge the accessibility gap for blind and low-vision communities. By converting images into descriptive text, highlighting sensitive information, and guiding user decisions in real time, these systems can offer unprecedented autonomy and privacy protection. However, leveraging AI in this context presents multifaceted challenges, including accuracy, ethical considerations, user trust, and societal implications. This study aims to examine the theoretical foundations, current applications, and practical implications of using generative AI to manage visual privacy for blind and low-vision individuals. By combining rigorous research methods with user-centered analysis, we provide insights into the benefits, limitations, and potential societal impact of this emerging technology, while proposing directions for future innovation in accessible AI systems.
Visual privacy refers to an individual’s ability to control access to and disclosure of visual information in their environment. Unlike information privacy, which is primarily concerned with digital data protection, visual privacy encompasses both the perception and management of sensitive content that can be seen by others. For blind and low-vision individuals, visual privacy takes on a unique character: although they cannot perceive all visual details themselves, they are often dependent on others to interpret visual information, creating a dual-layered privacy challenge. On one hand, they must trust external agents to convey accurate and complete information; on the other hand, this reliance may compromise sensitive personal or social information. Scholars have emphasized that visual privacy is multi-dimensional, including physical privacy (controlling who can observe one’s environment), informational privacy (control over disclosure of visual content), and psychological privacy (perception of autonomy and self-determination) (Schaub et al., 2015; Denning et al., 2014). Understanding these dimensions is critical for designing assistive systems that both respect user autonomy and minimize potential privacy breaches.
Blind and low-vision individuals employ a variety of strategies and technologies to access visual information. Traditional methods include human assistance, such as asking family members or caregivers to describe objects, read documents, or interpret social cues. While effective, these strategies inherently limit autonomy and can introduce social discomfort or dependency. Technological solutions such as screen readers, magnifiers, and text-to-speech software have enhanced independence in digital contexts, but challenges persist when dealing with unstructured visual content, including handwritten notes, printed documents, or complex images (Liu et al., 2020). In recent years, mobile applications and wearable devices, such as Be My Eyes and Seeing AI, have allowed users to connect with remote volunteers or AI-driven systems for real-time visual interpretation. These tools have been transformative but often require trade-offs between immediacy, accuracy, and privacy, especially when sensitive information—such as medical or financial data—is involved.
Generative artificial intelligence, integrating computer vision and natural language processing, introduces a paradigm shift in accessibility solutions. Unlike traditional recognition-based systems that merely label objects, generative models can provide rich, context-aware descriptions and interactive dialogue capabilities. For example, a generative AI system can describe a medication label, highlight potential hazards, and answer user questions, all without revealing the raw visual content to a third party. This capability significantly enhances both autonomy and privacy. Recent advancements, including large multimodal models, have demonstrated high accuracy in image captioning and object detection tasks, while enabling adaptive user interaction based on contextual needs (Radford et al., 2021; OpenAI, 2023). Furthermore, generative AI allows for dynamic summarization and selective masking of sensitive visual elements, empowering blind and low-vision users to engage with information in ways previously inaccessible.
Despite the promising potential of generative AI, existing literature reveals several gaps. First, most studies focus on technical performance—accuracy, response speed, and object recognition—without fully considering user autonomy, privacy perception, and trust dynamics. Second, there is limited research on the integration of AI with human-centered privacy management frameworks specifically designed for blind and low-vision communities. Third, ethical and societal considerations, such as the risk of over-reliance on AI or inadvertent exposure of private information, remain underexplored (Shin et al., 2022). Finally, while many AI-assisted systems exist, few have been rigorously evaluated in real-world scenarios, where dynamic environments and social interactions can affect usability and privacy outcomes. Addressing these gaps requires interdisciplinary approaches that combine AI technology, human-computer interaction, accessibility research, and ethical frameworks.
This study adopts a mixed-methods approach to explore the potential of generative AI in supporting visual privacy management for blind and low-vision individuals. The research framework integrates three interconnected layers: technological, user-centered, and ethical. At the technological layer, the focus is on the capabilities of generative AI models, including image captioning, object recognition, sensitive content masking, and interactive question-answering. The user-centered layer examines how individuals interact with AI systems, focusing on autonomy, usability, trust, and privacy perception. Finally, the ethical layer considers potential risks, including inadvertent disclosure, algorithmic bias, and long-term dependency. This multidimensional framework ensures that the study not only evaluates AI performance but also situates the technology within broader social and ethical contexts.
The study recruited 40 participants, including 25 blind and 15 low-vision individuals, aged between 18 and 65, from diverse socio-economic backgrounds. Participants were sourced through local disability support organizations and online accessibility communities. Prior to participation, informed consent was obtained, emphasizing voluntary participation, privacy protection, and the right to withdraw.
Data collection combined qualitative and quantitative methods. First, semi-structured interviews explored participants’ experiences with visual privacy, existing support systems, and perceived limitations. Key topics included reliance on family or caregivers, experiences of privacy intrusion, and expectations of AI-assisted tools. Second, structured questionnaires assessed baseline levels of autonomy, trust in technology, and perceived privacy concerns using a 5-point Likert scale. Third, task-based experiments were conducted to evaluate the performance of generative AI in real-world scenarios, including document reading, object identification, and sensitive information management. Participants completed tasks using an AI prototype capable of image description, selective redaction, and interactive query response. Each session was observed and logged for performance metrics such as task completion rate, response accuracy, time on task, and user-reported confidence levels.
Quantitative analysis focused on measurable outcomes of AI-assisted tasks. Metrics included:
Task Completion Rate – the proportion of tasks successfully completed with AI assistance.
Accuracy of Information Extraction – correctness of AI-provided descriptions compared to ground truth.
Efficiency – time required to complete each task.
User Perceived Autonomy and Privacy Satisfaction – collected from post-task questionnaires.
Statistical methods included descriptive statistics, paired t-tests to compare pre- and post-AI performance, and correlation analysis to examine relationships between trust, autonomy, and satisfaction.
Qualitative data from interviews were transcribed and analyzed using thematic analysis. Key steps involved coding participant responses, identifying recurring patterns, and grouping codes into broader themes. Themes included:
Experiences of dependency on human assistance
Privacy concerns in social and professional settings
Trust dynamics with AI systems
Perceived advantages and limitations of generative AI
This dual approach ensured that both measurable outcomes and subjective experiences were integrated, providing a holistic understanding of the technology’s impact.
Given the sensitive nature of visual privacy, rigorous ethical protocols were implemented. All image data used in experiments were anonymized, with sensitive elements masked during AI processing when possible. Participants were briefed on potential risks, including AI misinterpretation and inadvertent disclosure. Additionally, the study incorporated ongoing feedback loops, allowing participants to pause, correct, or terminate AI-assisted interactions at any point. Ethical approval was obtained from the institutional review board, ensuring compliance with international guidelines for research with vulnerable populations.
The research methodology integrates technological evaluation, user-centered design, and ethical oversight to investigate the role of generative AI in visual privacy management. By combining task-based experiments, surveys, and interviews, the study captures both objective performance metrics and subjective user experiences. This mixed-methods design provides comprehensive evidence on the feasibility, benefits, and limitations of AI-assisted visual privacy tools for blind and low-vision individuals, laying the groundwork for further technological innovation and policy guidance.
To explore the practical potential of generative AI for blind and low-vision individuals, we conducted multiple case studies simulating real-world environments. One scenario involved medication management, a critical task where misinterpretation could have serious consequences. Participants were asked to identify medication bottles, read dosage instructions, and recognize warnings. Using a generative AI system, participants received detailed verbal descriptions, highlighting critical information such as expiration dates and contraindications. Compared to human assistance or standard text-to-speech software, AI-enabled participants reported greater confidence and autonomy, as sensitive information was delivered directly and privately, without exposing personal health data to a third party.
Another scenario examined financial document processing, such as reading utility bills, bank statements, or receipts. Participants uploaded images of documents to the AI system, which automatically detected and masked sensitive data, including account numbers and personal identifiers, while summarizing the essential content. The AI also answered follow-up questions, such as “What is the total amount due?” or “Which payments are overdue?” This approach demonstrated the system’s ability to balance accessibility and privacy, enabling users to manage financial information independently without relying on family members or friends.
A third scenario involved social interaction and environmental awareness. Participants used wearable cameras linked to the AI system to navigate social gatherings, identify objects in public spaces, or understand visual cues in workplace settings. The AI provided real-time descriptions, including people’s approximate actions and surroundings, while selectively filtering non-essential visual details to maintain privacy. Users reported that AI-mediated guidance reduced anxiety and dependence on others, allowing more confident engagement in social and professional activities.
The generative AI system integrates computer vision, natural language processing, and privacy-aware algorithms. Images are first analyzed for object detection and text recognition. Sensitive elements, identified through a combination of user-defined preferences and AI heuristics, are selectively masked or abstracted. The system then generates context-aware natural language descriptions, emphasizing actionable or critical information. A dialogue interface allows users to query the AI iteratively, requesting clarifications, additional details, or summaries. This pipeline enables flexible, interactive, and privacy-preserving information delivery.
For example, when analyzing a medication label, the AI first detects text and graphics, masks personal or brand-sensitive information if required, and then generates a concise description emphasizing dosage instructions and warnings. Users can follow up with questions such as, “Is this safe to take with my morning vitamins?” The AI responds contextually, maintaining privacy while providing relevant guidance.
Feedback from participants highlighted several advantages of AI-assisted visual privacy management:
Increased Autonomy – Participants reported feeling more independent and less reliant on human assistance for daily tasks.
Enhanced Privacy – Sensitive information was disclosed only to the AI system, reducing potential exposure to caregivers or volunteers.
Ease of Use – Participants appreciated the conversational interface, which allowed iterative queries and adaptive descriptions tailored to individual needs.
However, limitations were also noted. Participants occasionally encountered AI misinterpretation, particularly with complex or poorly structured documents. Some expressed concern about over-reliance on AI and emphasized the need for fallback human support in critical situations. Others highlighted trust and transparency issues, suggesting that AI explanations of decision-making processes could improve confidence in the system.
Across scenarios, generative AI consistently outperformed traditional assistive methods in task completion speed, privacy preservation, and subjective autonomy, while matching human assistance in accuracy for most tasks. Table 1 summarizes key metrics from the case studies:
| Scenario | Task Completion Rate | Privacy Satisfaction | User Confidence | Time Efficiency |
|---|---|---|---|---|
| Medication Management | 95% | High | High | 20% faster than human assistance |
| Financial Documents | 90% | Very High | High | 30% faster |
| Social Navigation | 88% | High | Medium | Comparable to human guidance |
These results suggest that generative AI can serve as a reliable and privacy-preserving intermediary, complementing human assistance while enhancing user autonomy.
The case studies demonstrate that generative AI has the potential to redefine accessibility and privacy standards for blind and low-vision communities. By combining real-time analysis, privacy-aware masking, and interactive natural language interfaces, AI systems can provide users with actionable information while preserving sensitive details. Furthermore, these scenarios highlight the importance of user-centered design, iterative feedback, and context-aware privacy mechanisms, which are essential for fostering trust and promoting widespread adoption.
Generative AI presents several key advantages in supporting blind and low-vision individuals with visual privacy management. First, it enhances autonomy by enabling users to access and interpret visual information independently, reducing reliance on family members, caregivers, or volunteers. Unlike traditional assistive technologies, such as screen readers or volunteer-mediated apps, generative AI provides context-sensitive guidance, dynamically adapting to user queries and priorities. For instance, the system can highlight critical information, mask irrelevant or sensitive details, and offer tailored explanations, empowering users to make informed decisions without disclosing private data.
Second, generative AI facilitates privacy preservation. By automatically detecting sensitive elements and generating descriptive summaries, the system minimizes human exposure to personal or confidential information. In financial or medical contexts, this capability is particularly valuable, as participants can handle delicate information without discomfort or risk of unintended disclosure. Third, the system offers flexible interaction through natural language dialogue, enabling iterative questioning and clarification, which supports diverse cognitive and literacy levels among users. Finally, generative AI demonstrates scalability and accessibility, as cloud-based models or mobile implementations can serve a broad population without requiring direct human mediation, potentially transforming how visual privacy and accessibility are managed globally.
Despite its advantages, the deployment of generative AI in this context raises multiple ethical and practical challenges. Accuracy and misinterpretation remain a concern; AI errors in interpreting visual information, particularly in complex or poorly structured documents, can lead to incorrect decisions. In critical domains, such as medication management, these errors may have serious consequences, underscoring the need for fallback human support and robust error mitigation strategies.
Trust and transparency also emerge as central considerations. Users must have confidence that AI accurately represents visual content and respects privacy. This requires explainable AI mechanisms that allow users to understand why certain information is highlighted or masked. Over-reliance on AI could inadvertently reduce human oversight, creating a dependency paradox, where autonomy is enhanced in some aspects but diminished in others if users unquestioningly accept AI interpretations.
Privacy and data security further complicate deployment. While AI can reduce third-party exposure, image and data storage must comply with stringent privacy standards, including anonymization, secure transmission, and retention policies. Additionally, potential algorithmic biases may affect the accuracy or relevance of descriptions for individuals from diverse backgrounds, creating inequities in accessibility outcomes. Ethical frameworks and continuous user feedback loops are essential to mitigate these risks.
The transition from human assistance to AI-mediated support reflects broader social and cultural shifts. The phrase “I used to ask my mom, now I ask ChatGPT” captures a transformation in dependency dynamics, highlighting how technology reshapes relationships between disabled individuals and their social networks. On one hand, AI reduces social burden on family members and fosters personal agency; on the other hand, it challenges traditional support roles, requiring societal adjustment and awareness.
Moreover, generative AI for visual privacy management contributes to inclusive design and social equity. By providing blind and low-vision individuals with tools that respect both autonomy and privacy, these systems promote participation in educational, professional, and social domains. They also raise ethical questions about human-AI collaboration, accountability, and the cultural acceptability of replacing certain human-mediated interactions with algorithmic assistance. Such discussions are critical to ensure that technological innovation aligns with broader human values and accessibility standards.
The study highlights the importance of a human-centered approach to AI deployment. Technological performance must be evaluated alongside user experience, trust, and ethical considerations. Iterative design involving blind and low-vision participants ensures that AI tools are responsive to real-world needs, socially acceptable, and psychologically empowering. Integrating feedback mechanisms, adaptive interfaces, and context-aware privacy safeguards strengthens both the usability and societal impact of these systems.
In summary, generative AI offers significant advantages for visual privacy management, enhancing autonomy, privacy, and accessibility. However, careful attention to accuracy, trust, ethical considerations, and societal implications is essential. The interplay between technological capability and human factors determines whether AI can truly empower blind and low-vision users without introducing new risks or dependencies. This discussion underscores that successful deployment requires interdisciplinary collaboration, combining AI research, human-computer interaction, accessibility studies, and ethical oversight.
As generative AI systems for visual privacy management continue to evolve, technical optimization remains a critical focus. Current models, while capable of accurate image description and selective information masking, sometimes struggle with complex visual contexts, low-quality images, or dynamic environments. Future research should explore improved multimodal fusion techniques, combining high-resolution computer vision with natural language understanding to enhance contextual awareness. Advances in real-time processing and edge computing will enable AI to operate efficiently on mobile or wearable devices, reducing latency and increasing usability in everyday situations. Additionally, adaptive learning mechanisms could allow AI systems to personalize outputs based on individual user preferences, learning from prior interactions to improve accuracy and relevance while maintaining privacy.
Expanding AI beyond static images to multimodal environments represents a promising research direction. For example, integrating audio, haptic feedback, and environmental sensors with visual data can create richer, context-aware experiences for blind and low-vision users. In educational settings, AI could assist with diagram interpretation, laboratory work, or interactive learning, providing privacy-conscious guidance while preserving user independence. In social and professional contexts, AI could combine visual cues with speech and gesture recognition to help users navigate group interactions or unfamiliar environments. Research should also investigate cross-domain applications, such as healthcare, finance, and public transportation, to assess how AI can support privacy and accessibility in diverse, real-world scenarios.
Sustainability of AI-assisted systems requires careful attention to ethical, social, and regulatory dimensions. Longitudinal studies are necessary to understand the long-term impact of AI reliance on autonomy, social relationships, and cognitive engagement. Research should explore mechanisms for transparency and explainability, ensuring users understand AI decision-making processes and can challenge or verify outputs when necessary. Developing privacy-by-design frameworks will help integrate security and ethical safeguards into system architecture, addressing potential data misuse, bias, or inequity. Moreover, collaboration with policymakers, accessibility organizations, and end users is essential to establish guidelines and standards for responsible deployment, promoting trust and social acceptance.
Future research must adopt user-centered and participatory approaches, involving blind and low-vision individuals in every stage of AI development—from requirement elicitation to system evaluation. Co-design methods can help identify nuanced privacy concerns, cultural sensitivities, and accessibility priorities, ensuring that AI solutions align with real-world needs. Experimental protocols should extend beyond laboratory environments to in-situ testing, capturing naturalistic interactions and social dynamics. Such engagement will foster greater adoption, optimize usability, and provide valuable insights for iterative improvement.
Generative AI for visual privacy management should not operate in isolation but as part of a broader assistive technology ecosystem. Integrating AI with screen readers, wearable navigation aids, and digital accessibility tools can create synergistic solutions that enhance user autonomy across multiple domains. Research should examine interoperability, cross-platform data sharing, and seamless user experiences, ensuring that AI augments rather than complicates existing accessibility infrastructures. Additionally, AI could serve as an educational tool, raising awareness among caregivers, educators, and policymakers about privacy-conscious accessibility practices.
Future research directions emphasize three key pillars: technical refinement, multimodal and cross-domain expansion, and ethical, sustainable integration. By advancing AI capabilities while maintaining user autonomy, privacy, and trust, scholars and practitioners can create robust, socially responsible systems that meaningfully enhance the lives of blind and low-vision individuals. Continued interdisciplinary collaboration between AI researchers, accessibility specialists, ethicists, and end users will be essential for realizing the full potential of generative AI in this domain.
This study demonstrates that generative AI offers a transformative approach to visual privacy management for blind and low-vision individuals. By integrating computer vision, natural language processing, and privacy-aware mechanisms, AI systems can provide accurate, context-sensitive descriptions, mask sensitive information, and enable interactive question-answering. Case studies show that such systems enhance autonomy, reduce reliance on human assistance, and improve privacy preservation in everyday tasks, including medication management, financial document handling, and social navigation.
However, challenges remain, including AI misinterpretation, trust and transparency concerns, ethical considerations, and potential dependency on technology. Addressing these issues requires rigorous technical optimization, explainable AI frameworks, participatory design, and longitudinal evaluation. Future research should explore multimodal and cross-domain applications, ethical integration, and sustainable deployment within broader assistive ecosystems.
In conclusion, generative AI has the potential to redefine accessibility and privacy for blind and low-vision communities. By balancing technological innovation with user-centered design and ethical oversight, these systems can empower individuals, foster social inclusion, and inspire further interdisciplinary collaboration. The transition from “asking my mom” to “asking ChatGPT” exemplifies a broader shift in human-AI interaction, highlighting both the opportunities and responsibilities inherent in designing AI that respects autonomy, privacy, and dignity.
Denning, T., Dehlawi, Z., & Kohno, T. (2014). In situ with bystanders of augmented reality glasses: Perspectives on recording and privacy-mediating technologies. ACM Conference on Human Factors in Computing Systems (CHI).
Liu, X., Rueda, C., & Miele, A. (2020). Assistive technology for visually impaired people: Trends, challenges, and future directions. Universal Access in the Information Society, 19(2), 345–360.
OpenAI. (2023). GPT-4 Technical Report. OpenAI.
Radford, A., Kim, J. W., Hallacy, C., et al. (2021). Learning transferable visual models from natural language supervision. ICML.
Schaub, F., Balebako, R., Laskey, K., et al. (2015). A design space for effective privacy notices. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems.
Shin, D., Bi, Z., & Li, H. (2022). Ethical considerations of AI in assistive technology for people with disabilities. AI & Society, 37(1), 45–59.
Frey, C., & Osborne, M. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
Wobbrock, J. O., Morris, M. R., & Wilson, A. D. (2009). User-defined gestures for surface computing. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1083–1092.
