2024/03/05 by Aashaka Desai, Maartje De Meulder, Desai, Aashaka +8 · 1 voice · 3 citations
Computer Science · Psychology · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hearing Impairment and Communication #cs.CL #cs.CV
paper · pdf · doi:10.48550/arxiv.2403.02563
openalex publication_date 2024/03/05 · arxiv published 2024/03/05 · arxiv updated 2024/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.