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"Better Than Nothing" or Not Enough? User-Centered Reflections on AI-Generated Audio Descriptions Across Media
Diana Yee1, Amina Kobenova2, Rohan Jhangiani3
1Computational Media University of California, Santa Cruz Santa Cruz, California, USA.
ASSETS. Annual ACM Conference on Assistive Technologies
|June 3, 2026
Summary
AI-generated audio descriptions (ADs) improve media access for blind and low-vision (BLV) individuals. However, BLV users find AI ADs lack emotional depth and context, highlighting needs for user-informed design.
Area of Science:
- Human-Computer Interaction
- Accessibility Research
- Media Studies
Background:
- AI-generated audio descriptions (ADs) present scalable solutions for media accessibility.
- Limited understanding exists regarding blind and low-vision (BLV) users' experiences with AI ADs across diverse platforms.
Purpose of the Study:
- To explore BLV users' perceptions of human- and AI-generated ADs.
- To investigate user experiences across various media formats, including film, short-form video, and livestreams.
Main Methods:
- Qualitative study employing semi-structured interviews with ten BLV participants.
- Thematic analysis of interview data to identify key user experiences and perceptions.
Main Results:
- Five themes emerged: information prioritization, AI ADs' "better-than-nothing" status with emotional/contextual gaps, social viewing dynamics, platform accessibility issues, and the artistry-precision trade-off.
- AI ADs are perceived as useful but often lack nuanced emotional and contextual details compared to human-generated ADs.
Conclusions:
- There is a need for adaptive, transparent, and user-informed AD systems balancing narrative quality with efficiency.
- Co-designing AI-assisted accessibility tools with BLV communities is crucial for effective solutions.
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