AI Diffusion in Low Resource Language Countries

TL;DR

This paper investigates how linguistic accessibility impacts AI adoption in low-resource language countries, revealing a significant barrier that reduces AI usage by about 20% compared to expectations based on socioeconomic factors.

Contribution

It provides empirical evidence that linguistic barriers independently hinder AI diffusion in low-resource language countries, highlighting a critical area for improving equitable AI access.

Findings

LRLCs have about 20% lower AI user share than expected

Linguistic accessibility is an independent barrier to AI diffusion

Performance deficits of LLMs in LRLCs contribute to lower AI adoption

Abstract

Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-resource languages due to data scarcity. We hypothesize that this performance deficit reduces the utility of AI, thereby slowing adoption in Low-Resource Language Countries (LRLCs). To test this, we use a weighted regression model to isolate the language effect from socioeconomic and demographic factors, finding that LRLCs have a share of AI users that is approximately 20% lower relative to their baseline. These results indicate that linguistic accessibility is a significant, independent barrier to equitable AI diffusion.