Arabic-First AI Starts With Context, Not Translation
An AURENDOR adaptation of the World Bank's argument that locally relevant context is a foundation for effective AI.
Based on research by
Yan Liu et al. — World Bank
Published -
The World Bank's 2025 report describes context as one of four foundations for effective AI. For organizations in Iraq and the region, context is not a language toggle — it includes terminology, document habits, institutions, permissions, and the way Arabic and English mix in daily work.
Arabic-first AI is designed around local evidence and local work, not merely translated after the system is finished.
Translation is not localization
The World Bank identifies connectivity, compute, context, and competency as the four Cs of AI foundations. Its discussion of context emphasizes the quantity, quality, and diversity of data — and the need to adapt systems to local economic, cultural, and institutional realities.
What local context includes
For organizations in Iraq and the region, local context appears in language, records, names, dates, approval structures, and the standards people use to judge whether an answer is correct.
Arabic terminology actually used by the team and its customers.
Mixed Arabic-English records, names, numbers, dates, and technical language.
Local institutional rules, approval structures, and measures of a correct answer.
Build with local evaluation
A system should be tested with real regional documents and real users before it is called Arabic-first. Local evaluation reveals retrieval failures, ambiguous terms, formatting problems, and cultural assumptions that a translated interface cannot solve.
Source: World Bank — Digital Progress and Trends Report 2025. This is an adaptation of an original work by The World Bank, licensed CC BY 3.0 IGO. Views and opinions in this adaptation are AURENDOR’s and are not endorsed by The World Bank. Reuse terms
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