African languages vary widely in readiness for AI safety research
African Languages AI Safety Readiness Report
Computation and Language
Summary
Many African languages have less data and support for helping make AI systems safer. The authors looked at how ready these languages are for AI safety work by checking resources, policies, data performance, and number of speakers. They grouped the languages into four categories based on their readiness levels. This helps focus effort on improving AI safety for languages that are less prepared.
What this means in practice
- •For ai policy makers: Identify which African languages need more policy and funding support to prepare for AI safety inclusion.
- •For language technology developers: Target development of language data and benchmarks on African languages with emerging or foundational readiness to enhance AI safety coverage.
A survey. It maps existing work.
Authors
Israel Abebe Azime, Jesujoba O. Alabi, Tajuddeen Gwadabe, David Ifeoluwa Adelani
Abstract
With the widespread adoption of large language models (LLMs) across languages and domains, AI safety has emerged as a critical global concern. However, due to resource constraints, research and development efforts tend to prioritize a limited number of high-resource languages, leaving many others, especially those spoken in Africa, underrepresented. While existing research on language "resource-ness" has largely examined whether sufficient resources are available to develop language technologies, the question of whether languages are adequately represented in AI safety research has received attention only more recently. This report examines the readiness of African languages for inclusion in AI safety discussions, drawing on existing literature and data. It considers four factors: the availability of AI-relevant resources, including text data; national AI policy support; performance on established text benchmarks; and speaker population. Based on these factors, languages are grouped into four readiness categories: fully ready, mostly ready, emerging, and foundational support needed. This classification is intended to inform the inclusion of African languages in AI safety benchmarks, highlight varying levels of preparedness, and guide strategic investment in data development, policy, and community engagement for underrepresented languages.