The proliferation of Generative Artificial Intelligence (GenAI) tools has brought a critical shift in how people approach information retrieval and content creation in diverse contexts. Yet, we have limited understanding of how blind and low vision people use and make sense of GenAI tools. In a recent study, we interviewed 19 blind individuals to understand how they incorporate mainstream GenAI tools like ChatGPT, Microsoft Copilot, Google Gemini, Claude, and Be My AI in their everyday practices. Our findings revealed how blind users navigate accessibility issues, inaccuracies, hallucinations, and idiosyncracies associated with GenAI and develop interesting (but often flawed) mental models of how these tools work. We discussed key considerations for rethinking access and information verification in GenAI tools, unpacking erroneous mental models among blind users, and reconciling harms and benefits of GenAI from an accessibility perspective.

In another study, conducted in collaboration with Google researchers, we focused on accessibility of AI-generated images. State-of-the-art image generation models do not output alternative (alt) text with their images, rendering them largely inaccessible to screen reader users. Moreover, we know less about what information about this new visual medium would be most desirable to screen reader users. To address this, we invited AI image creators and SRUs to evaluate alt text prepared from various sources and write their own alt text for AI images. Through a mixed-method analyses, we illustrated creators perspectives and screen reader users’ information needs from alt text of AI images. Finally, we discussed the promises and pitfalls of utilizing text prompts written as input for AI models in alt text generation, and areas where broader digital accessibility guidelines could expand to account for AI images.

Relevant Publications

Rudaiba Adnin and Maitraye Das. 2024. “I look at it as the king of knowledge”: How Blind People Use and Understand Generative AI tools. To appear in Proceedings of the International ACM SIGACCESS Conference on Computers and Accessibility, (ASSETS ’24).

Maitraye Das, Alex Fiannaca, Meredith Ringel Morris, Shaun Kane, and Cynthia Bennett. 2024. From Provenance to Aberrations: Image Creator and Screen Reader User Perspectives on Alt Text for AI-Generated Images. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’24)[ACM DL Link][Video]