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Exploring Collaboration to Center the Deaf Community in Sign Language AI.

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Summary
This summary is machine-generated.

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.

Keywords:
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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.