Cross-Cultural Challenges in AI Translation for Deaf and Non-Verbal Populations

Artificial intelligence (AI) translation systems, which seek to address communication barriers among Deaf and Hard-of-Hearing (DHH) communities, often experience repeat cultural failure based on their incompetence to realize the rich cultural complexity and emotional depth embodied in the signed languages.A systematic study indicates recognizable patterns of cultural error, such as the threatening normalization of interpreted, non-native data and the failure to focus on the sensitive linguistic aspects, especially non-manual signs that bear grammatical and affective significance and meaning.In order to address these systemic failures, this paper suggests a Cultural Context and Error Framework of AI Translation (CCEF-AI) including an evaluation taxonomy, which recommends the mandatory addition of layers of cultural metadata, hybrid human-AI collaboration on high-stakes settings, and metrics that go beyond superficial measures of accuracy rating.Finally, equitable access would require a general effort towards inclusive, adaptive, community-informed AI design where Deaf leadership leads the agenda and development processes of research to ensure the erosion of linguistic rights and systemic bias are prevented by implementing researchbased interventions and solutions.