The multilingual AI gap has not narrowed, just been repackaged

The multilingual AI gap has not narrowed, just been repackaged beneath the surface of data coverage and benchmark scores. The SAHARA benchmark evaluates 517 African languages across 16 NLP tasks, confirming a significant performance gap between English and most African languages; a 2026 test found that guardrails effective in English, when moved to West African languages, saw refusal rates drop by more than half in some cases. UbuntuGuard, built by 155 experts from 6 countries, shows that English-centric benchmarks overestimate multilingual safety.