RADAR: Readiness for AI Discovery and Agentic Reach

arXiv:2609.28480v1 Announce Type: new 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.

Measures AI effectiveness in public services

The RADAR framework highlights a gap between AI describing public services and actually enabling access to them. This has potential to improve public service efficiency and accessibility if addressed, yet current limitations could hinder citizen interaction. The impact is positive but constrained by existing challenges and uneven government capabilities.

This is a measurement study revealing a real but fixable gap in AI-mediated government service access, not a claim about AI causing broad societal harm. The implication is mildly positive in that it identifies an actionable problem, though the gap itself signals a mild equity risk if unaddressed.

The paper highlights a significant gap in AI's ability to provide actionable access to public services, despite being able to describe them. This represents a tangible barrier to citizen interaction with government, which could worsen if not addressed. However, it also offers a clear path for governments to improve reachability.

The paper presents a tool that helps governments improve AI's ability to reach public services. This can enhance access to public services, benefiting citizens. The findings are based on a large-scale study across 166 countries.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   ef9682da-bdc5-4525-a4ae-9e6be18cbc1a
           https://arxiv.org/abs/2609.28480
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -1  (Mixed or uncertain)
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CONFIDENCE 77%