低资源语言国家AI普及率低，主要因语言障碍导致模型效果差。
AI Diffusion in Low Resource Language Countries
- 用加权回归分离语言与经济因素影响
- 低资源语言国家用户占比比基准低约20%
- 强调语言可及性是公平AI扩散的关键障碍
人工智能正以前所未有的速度全球扩散，但应用仍不均衡。前沿大语言模型在低资源语言上表现不佳，主要因数据稀缺。我们假设这种性能差距降低了AI的实用性，从而减缓了低资源语言国家（LRLCs）的采纳速度。通过加权回归模型，我们隔离了语言因素与社会经济、人口因素的影响，发现LRLCs的AI用户占比相比其基准水平低约20%。结果表明，语言可及性是阻碍公平AI扩散的一个显著且独立的障碍。
原文摘要 · Abstract (English)
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.
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