Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages

Artificial intelligence tools for education and language support are increasingly framed as scalable responses to access gaps in under-resourced communities. Yet the infrastructure underlying these tools, including training corpora, tokenization schemes, evaluation benchmarks, and deployment architectures, can systematically disadvantage speakers of underrepresented languages before a model is trained. This paper examines these structural barriers through Bengali, one of the world's most widely

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ethics.ai (12 August 2026), “Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages,” evidence record 19025, https://ethics.ai/record/19025 (originally published by arXiv cs.AI).

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