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.