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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 13·5 min read
Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages
TL;DR
AI tools for education often overlook underrepresented languages, leading to significant disparities in access and support. This paper highlights the structural barriers faced by Bengali speakers, emphasizing the need for equitable AI infrastructure.
✦ Why It Matters
Evaluate your AI projects for inclusivity and consider offline-first strategies to support underrepresented languages.
Key Takeaways
- Assess web content representation for underrepresented languages in your projects.
- Consider the impact of tokenization on language processing models.
- Implement offline-first strategies to enhance accessibility in low-connectivity areas.
- Advocate for equitable resource allocation in AI development.
Full Summary
AI technologies are increasingly seen as solutions to educational access gaps in under-resourced communities, yet the underlying infrastructure often disadvantages speakers of underrepresented languages. Focusing on Bengali, which is spoken by nearly 4% of the global population but accounts for less than 0.5% of web content, the paper identifies four key failures: a significant web presence gap, a 67:1 training-token deficit compared to English, a tokenization penalty due to Bengali's alphasyllabary script, and connectivity issues in rural areas.
These barriers stem from historical resource allocation and design choices that neglect these languages. The authors argue that dataset scarcity is a structural issue rather than a mere technical limitation and advocate for offline-first design as a strategy for equity.
The paper concludes with recommendations for linguistics and AI research to address these inequalities.