Summary of "Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages"
*Roy, A., & Roy, P. (2026). arXiv:2608.12278*
This review covers a case-study paper analyzing how contemporary AI systems systematically exclude Bengali speakers through four interlocking mechanisms of structural silence.
Key points
- Web invisibility: Bengali accounts for roughly 4% of the world's population but under 0.5% of web content (Pimienta, 2024; W3Techs, 2026). Each English-native user produces about 100 times more web content than each Bengali-native user, a disparity rooted in colonial linguistic legacy, English-centric technical infrastructure, and platform economics.
- Data poverty: Major multilingual corpora allocate only about 30 billion tokens to Bengali versus roughly 2 trillion for English, a 67:1 ratio. Chang et al. (2024) found that large multilingual models can perform worse than simple bigram baselines on low-resource languages, indicating that architectural complexity cannot compensate for insufficient data.
- Self-reinforcing cycle: Poor Bengali performance leads to low adoption, which reduces commercial investment, which stalls data collection, which further degrades performance. The authors argue this cycle is the result of institutional choices in research funding, academic publishing incentives, and corporate market prioritization, not natural market dynamics.
- Tokenizer discrimination: Bengali's alphasyllabary script with matras, conjuncts (yuktakshar), and multi-character grapheme clusters is poorly served by BPE and WordPiece tokenizers optimized for Latin scripts. The resulting high *token fertility* increases training cost, slows inference, shortens effective context windows, and degrades semantic coherence, making disproportionately more data necessary to achieve parity even when raw volume is equalized.
- Connectivity caste system: Bangladesh Bureau of Statistics (2024–25) data show rural personal internet penetration at 36.5% versus 71.4% urban, only 9.2% of rural households own a computer, and mobile data surcharges rose from 3% (FY2016) to 23% (2024). Cloud-first AI educational tools are functionally inaccessible to most rural learners.
- Cognitive double burden: Cognitive load theory (Sweller et al., 2011) and studies by Roussel et al. (2017) and Soosai Raj et al. (2018) show that bilingual learners processing technical content in a second language suffer reduced comprehension and knowledge retention. Mother-tongue AI is therefore a neuroscientific prerequisite for meaningful education, not a localization extra.
- Roy, A., & Roy, P. (2026). *Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages*. arXiv:2608.12278
- Pimienta, D. (2024). *Web Presence and Language Diversity*
- W3Techs. (2026). *Usage Statistics of Content Languages for Websites*
- Khan et al. (2024). *Sangraha: A Large-Scale Multilingual Corpus*
- Langlais et al. (2025). *Common Corpus: Trillion-Token Dataset for Language Modeling*
- Chang et al. (2024). *Low-Resource Language Performance in Multilingual Models*
- Kabir et al. (2024). *Benchmarking Bengali NLP*
- Bhowmik et al. (2025). *Multilingual LLM Performance on Bengali Tasks*
- Sweller et al. (2011). *Cognitive Load Theory*
- Roussel et al. (2017). *Language of Instruction and Technical Learning*
- Soosai Raj et al. (2018). *Mother-Tongue Education and Knowledge Retention*
- Bangladesh Bureau of Statistics (2024–25). *Digital Access Survey*
Proposed interventions
1. Reframe data scarcity as institutional choice: Treat foundational dataset construction, translation validation, and benchmark development for low-resource languages as first-class research contributions equal to architectural innovation; reform academic evaluation systems accordingly.
2. Offline-first as equity infrastructure: Design and evaluate educational AI under bandwidth, device, and cost constraints; build lightweight models for low-power devices; treat local quantized inference as both fairer and more sustainable than cloud-routed APIs.
3. Linguistics as a core AI discipline: Engage applied sociolinguistics, linguistic typology, and critical language studies to surface hidden assumptions baked into tokenizers, benchmarks, and deployment defaults.
Conclusion
The authors argue that performance gaps in underrepresented languages reflect accumulated, non-neutral decisions about data, evaluation, and deployment. Closing these gaps requires infrastructure-level intervention, not incremental model improvements.