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May 5, 20260 citationsOpen Access

Structural Silence: When AI Infrastructure Fails the World's Underrepresented Languages — Poster Presented at ILA 2026

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ARAvijit RoyPRProma Roy

Key Points

  • This research aims to identify barriers that hinder the inclusion of underrepresented languages in AI systems, using Bengali as a focal point.
  • Examined systemic constraints affecting AI performance for Bengali, including web presence disparity and multilingual training token imbalance.
  • Analyzed the impact of tokenization overhead and connectivity exclusion on model accessibility.
  • Proposed offline-first design strategies and transparent evaluation practices to address these issues.
  • Identified four systemic constraints leading to performance asymmetries in AI models for underrepresented languages.
  • Argued that dataset scarcity is a structural issue influenced by historical and economic factors.
  • Emphasized the need for recognizing dataset construction as essential for advancing multilingual AI.

Abstract

Structural Silence examines the structural barriers that limit participation of underrepresented languages in modern AI systems. Using Bengali as a case study, this poster highlights four systemic constraints: web presence disparity, multilingual training token imbalance, tokenization overhead, and connectivity exclusion. Together, these factors create measurable asymmetries in model performance and accessibility. The poster argues that dataset scarcity is not merely a technical limitation but a structural infrastructure issue shaped by historical and economic forces. It calls for offline-first design strategies, transparent multilingual evaluation practices, and formal recognition of dataset construction as a core research contribution. This poster was presented at the 69th Annual Conference of the International Linguistic Association (ILA 2026) at John Jay College of Criminal Justice, New York, NY, USA.

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Cite This Study

Roy et al. (2026) studied this question.

synapsesocial.com/papers/69f9894115588823dae18293https://doi.org/10.5281/zenodo.19991978
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