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The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective

Authors:
Danielle Bragg

Microsoft Research, USA

Microsoft Research, USA
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,
Naomi Caselli

Boston University, USA

Boston University, USA
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,
Julie A. Hochgesang

Gallaudet University, USA

Gallaudet University, USA
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,
Matt Huenerfauth

Rochester Institute of Technology, USA

Rochester Institute of Technology, USA
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,
Leah Katz-Hernandez

Microsoft, USA

Microsoft, USA
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,
Oscar Koller

Microsoft, Germany

Microsoft, Germany
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,
Raja Kushalnagar

Gallaudet University, USA

Gallaudet University, USA

0000-0002-0493-413X
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,
Christian Vogler

Gallaudet University, USA

Gallaudet University, USA
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,
Richard E. Ladner

University of Washington, USA

University of Washington, USA
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Authors Info & Claims

ACM Transactions on Accessible Computing Volume 14 Issue 2Article No.: 7pp 1–45https://doi.org/10.1145/3436996

Published:21 July 2021Publication History

ACM Transactions on Accessible Computing

Abstract

Sign language datasets are essential to developing many sign language technologies. In particular, datasets are required for training artificial intelligence (AI) and machine learning (ML) systems. Though the idea of using AI/ML for sign languages is not new, technology has now advanced to a point where developing such sign language technologies is becoming increasingly tractable. This critical juncture provides an opportunity to be thoughtful about an array of Fairness, Accountability, Transparency, and Ethics (FATE) considerations. Sign language datasets typically contain recordings of people signing, which is highly personal. The rights and responsibilities of the parties involved in data collection and storage are also complex and involve individual data contributors, data collectors or owners, and data users who may interact through a variety of exchange and access mechanisms. Deaf community members (and signers, more generally) are also central stakeholders in any end applications of sign language data. The centrality of sign language to deaf culture identity, coupled with a history of oppression, makes usage by technologists particularly sensitive. This piece presents many of these issues that characterize working with sign language AI datasets, based on the authors’ experiences living, working, and studying in this space.

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Index Terms

The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective
1. Applied computing
  1. Enterprise computing
  2. Law, social and behavioral sciences
2. General and reference
  1. Document types
    1. General literature
    2. Reference works

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Published in
ACM Transactions on Accessible Computing Volume 14, Issue 2
June 2021
174 pages
ISSN:1936-7228
EISSN:1936-7236
DOI:10.1145/3477222
Editors:
Tiago Guerreiro
Universidade de Lisboa, Portugal
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Stephanie Ludi
University of North Texas, USA
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Publication History
- Published: 21 July 2021
- Accepted: 1 November 2020
- Received: 1 August 2020
Published in taccess Volume 14, Issue 2

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The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective

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