African Languages AI Safety Readiness Report

Key points

• With the widespread adoption of large language models (LLMs) across languages and domains, AI safety has emerged as a critical global concern.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]African Languages AI Safety Readiness Report
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:43 PM

  This report examines the readiness of African languages for inclusion in AI safety discussions, drawing on existing literature and data.

  With the widespread adoption of large language models (LLMs) across languages and domains, AI safety has emerged as a critical global concern.

Extractive summary: sentences quoted from the sources.