RADAR: Readiness for AI Discovery and Agentic Reach

Abstract:Governments increasingly meet citizens through an AI system rather than a website. RADAR (Readiness for AI Discovery and Agentic Reach) measures whether that system works, across 166 countries and on two tasks: whether a chatbot can give a correct, officially sourced, country-specific answer about a public service (informational legibility), and whether an automated agent can reach the service to act on it (agent operability). The central finding is that AI can describe public services far better than it can reach them. In every one of the 166 countries, informational legibility scores exceed average agent operability scores under RADAR's respective measures, and the gap does not shrink with national wealth. Income and language explain only part of the pattern, and several governments perform far better or worse than their resources predict. The two failures have different correlates and different fixes. Whether AI can describe a service is associated with how well a country's main administrative language is represented in web-scale corpora, which a government cannot change quickly. Whether an agent can reach it is associated with the country's national web presence, which a government can change now. Traditional digital-government rankings miss the second problem entirely. RADAR lets governments at any income level see it and offers a concrete agenda to fix it, so that public services are not only described by AI but actually reachable through it.

Computers and Society (cs.CY); Machine Learning (cs.LG)

arXiv:2609.28480 [cs.CY]

(or arXiv:2609.28480v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2609.28480 Focus to learn more arXiv-issued DOI via DataCite

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