Maartje De Meulder

Maartje De Meulder

This paper explores the interplay of AI language technologies, sign language interpreting, and linguistic access, highlighting the complex interdependencies shaping access frameworks and the tradeoffs these technologies bring. While AI tools promise innovation, they also perpetuate biases, reinforce technoableism, and deepen inequalities through systemic and design flaws. The historical and contemporary privileging of sign language interpreting as the dominant access model, and the broader inclusion ideologies it reflects, shape AIs development and deployment, often sidelining deaf languaging practices and introducing new forms of linguistic subordination to technology. Drawing on Deaf Studies, Sign Language Interpreting Studies, and crip technoscience, this paper critiques the framing of AI as a substitute for interpreters and examines its implications for access hierarchies. It calls for deaf-led approaches to foster AI systems that remain equitable, inclusive, and trustworthy, supporting rather than undermining linguistic autonomy and contributing to deaf aligned futures.

Maartje De Meulder, Davy Van Landuyt, Rehana Omardeen

In the era of AI-driven language technologies, the participation of deaf communities in sign language technology development, often framed as co-creation, is increasingly emphasized. We present a reflexive case study of two Horizon 2020 projects on sign language machine translation (2021- 2023), conducted with a EUD, a European-level deaf-led NGO. Using participant observation, internal documentation, and collaborative analysis among the authors, we interrogate co-creation as both a practice and a discourse. We offer five lessons for making co-creation consequential: 1) recognise and resource deaf partners invisible labor, 2) manage expectations via accessible science communication, 3) crip co-creation by dismantling structural ableism, 4) diversify participatory methods to address co-creation fatigue and intersectionality, and 5) redistribute power through deaf leadership. We contribute an empirically grounded account of how co-creation plays out in multi-partner AI projects, and actionable implications for design that extend to participatory AI with minoritized language and disability communities.

Aashaka Desai, Maartje De Meulder, Julie A. Hochgesang et al.

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.