Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages
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Computer Science > Computation and Language
arXiv:2608.12278 (cs)
[Submitted on 12 Aug 2026]
Title:Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages
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Abstract: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 spoken languages, focusing on AI-assisted education in low-connectivity environments. We identify four interlocking failures: a severe web presence gap, with Bengali accounting for less than 0.5% of global web content despite representing nearly 4% of the global population; a 67:1 training-token deficit between English and Bengali in major multilingual corpora; a tokenization penalty associated with Bengali's alphasyllabary script that compounds the data deficit through higher token fertility; and connectivity exclusion, with individual internet penetration at 36.5% in rural areas compared with 71.4% in urban areas.
These failures reflect longstanding resource-allocation decisions, institutional priorities, and design defaults that did not center underrepresented languages in mainstream AI development. We argue that dataset scarcity should be understood as a structural barrier rather than an isolated technical limitation, and that offline-first design should be treated as an equity-oriented infrastructure strategy. We conclude with directions for linguistics and AI research aimed at reducing these structural inequalities.
| Comments: | An associated poster version of this work was presented at the 69th Annual Conference of the International Linguistic Association (ILA 2026), New York, NY, April 30-May 2, 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2608.12278 [cs.CL] |
| (or arXiv:2608.12278v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.12278
arXiv-issued DOI via DataCite (pending registration)
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