AI Diffusion in Low Resource Language Countries
This addresses the issue of equitable AI diffusion for populations in low-resource language countries, highlighting a significant barrier, though it is incremental in analyzing existing data.
The study tackled the problem of uneven AI adoption in low-resource language countries by testing the hypothesis that poor LLM performance due to data scarcity reduces utility and slows adoption, finding that these countries have about 20% lower AI user share relative to baseline.
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.