Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas
This addresses the problem of exclusion and bias in sign language AI research for Deaf communities, highlighting the need for systemic change rather than incremental improvements.
The paper identifies systemic biases in sign language AI research through a systematic review of 101 papers, finding issues like overfocus on perceived communication barriers, unrepresentative datasets, and flawed methods, and calls for Deaf-led research agendas.
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.