arXiv Machine Learning

RADAR: Readiness for AI Discovery and Agentic Reach

RADAR (Readiness for AI Discovery and Agentic Reach) evaluates how well AI systems serve citizens in 166 countries by testing two tasks: whether a chatbot can provide correct, officially sourced, country‑specific answers about public services (informational legibility) and whether an automated agent can actually access those services (agent operability). The study finds that AI can describe public services much better than it can reach them, with informational legibility consistently higher than agent operability across all countries and the gap remaining unchanged by national wealth. The two deficiencies have distinct causes—language representation in web corpora for legibility and national web presence for operability—and therefore require different solutions. "whyItMatters":"RADAR highlights a gap that traditional digital‑government rankings overlook, enabling governments of any income level to identify and address the specific barriers preventing AI from actually accessing public services."

arXiv AI
Jul 1

A Technical Typology of AI Systems in Public Administration

arXiv:2606. 31755v1 Announce Type: cross Abstract: Research on artificial intelligence (AI) in the public sector often treats "AI" as a single category, neglecting technical distinctions between different AI systems.

By Jonathan Rystr{\o}m, Chris Schmitz, Nathan Davies, Gerhard Hammerschmid, Albert Meijer, Chris Russell
arXiv AI
Jul 17

Global Index on Responsible AI: 2026 Report

arXiv:2607. 14782v1 Announce Type: new Abstract: Grounded in human rights-based frameworks such as the UNESCO Recommendation on the Ethics of AI, the Global Index on Responsible AI (GIRAI) examines how countries translate responsible AI commitments into enforceable protections, institutional capacity, and redress mechanisms.

By Rachel Adams, Fola Adeleke, Ayantola Alayande, Selamawit Engida Abdella, Ana Florido, Nicol\'as Grossman, Leah Junck
arXiv AI
Jun 16

Artificial Intelligence Index Report 2026

arXiv:2606. 15708v1 Announce Type: new Abstract: Welcome to the ninth edition of the AI Index report.

By Sha Sajadieh, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Lapo Santarlasci, Juan Pava, Nestor Maslej, Russ Altman, Erik Brynjolfsson, Carla Brodley, Jack Clark, Virginia Dignum, Vipin Kumar, James Landay, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Elham Tabassi, Russell Wald, Toby Walsh, Dan Weld
arXiv AI
Sep 16

Mapping U.S. Federal AI Governance Against Sector Vulnerability

The study evaluates 684 U.S. federal AI governance documents for how they address 14 sectors and 24 AI risks, measuring both breadth and depth of coverage. It finds that risks related to robustness, system security, and governance are more frequently and substantively discussed than socioeconomic, environmental, and emerging risks, and that sectors such as public administration, national security, information, and scientific services receive higher coverage than finance and healthcare. By comparing these coverage patterns with expert vulnerability assessments, the authors identify potential gaps in AI governance that could inform future policy and industry decisions.

By Ho Ting Hung, Angelica Chowdhury, James Teague, Simon Mylius, Spencer Michaels, Peter Slattery, Alexander Saeri, Neil Thompson