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Exploring Collaboration to Center the Deaf Community in Sign Language AI
Rie Kamikubo1, Abraham Glasser2, Alex X Lu3
1University of Maryland, College Park, Maryland, USA.
ASSETS. Annual ACM Conference on Assistive Technologies
|March 5, 2026
Summary
This study bridges AI development and Deaf community needs by identifying collaboration barriers. Findings offer resources for equitable, community-centered AI advancements in sign language processing.
Area of Science:
- Artificial Intelligence
- Human-Computer Interaction
- Sociolinguistics
Background:
- Sign language processing (SLP) aims for inclusivity but often excludes Deaf communities.
- Ethical and practical concerns arise regarding AI solutions' applicability to Deaf users' needs.
Purpose of the Study:
- To address gaps in AI development for the Deaf community.
- To identify differences between machine learning (ML) practitioners and Deaf American Sign Language (ASL) signers.
- To generate guiding questions for aligning AI development with community goals.
Main Methods:
- Surveys to compare ML practitioners' and Deaf ASL signers' priorities.
- Paired co-design sessions involving ML and ASL experts.
Main Results:
- Identified critical friction points and systemic/epistemic barriers in collaboration.
- Highlighted unique and shared insights between ML practitioners and Deaf ASL signers.
Conclusions:
- Emphasized the need for collaborative frameworks that promote Deaf community agency and expertise.
- Paved actionable pathways for equitable, community-centered AI advancements in SLP.
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