Systemic Biases in Sign Language AI Research: A Deaf-Led Call to
  Reevaluate Research Agendas
Aashaka Desai Maartje De Meulder Julie A. Hochgesang Annemarie Kocab Alex X. Lu
Growing research in sign language recognition, generation, and translation AI
has been accompanied by calls for ethical development of such technologies.
While these works are crucial to helping individual researchers do better,
there is a notable lack of discussion of systemic biases or analysis of
rhetoric that shape the research questions and methods in the field, especially
as it remains dominated by hearing non-signing researchers. Therefore, we
conduct a systematic review of 101 recent papers in sign language AI. Our
analysis identifies significant biases in the current state of sign language AI
research, including an overfocus on addressing perceived communication
barriers, a lack of use of representative datasets, use of annotations lacking
linguistic foundations, and development of methods that build on flawed models.
We take the position that the field lacks meaningful input from Deaf
stakeholders, and is instead driven by what decisions are the most convenient
or perceived as important to hearing researchers. We end with a call to action:
the field must make space for Deaf researchers to lead the conversation in sign
language AI.