A Global Comparison of Schemas, Transparency, and Interoperability in Public-Sector AI Registers and Inventories

2026-09-21

AI Summary

研究通过比较72个国家的8,368条AI记录，分析了公共部门AI注册表和清单在透明度、互操作性上的差异，并提出了解决数据共享问题的框架。

Abstract

Artificial intelligence (AI) registers and inventories aim to make governmental AI visible, but their institutional scope, schemas, and reporting practices construct different representations of public-sector AI. We compare 8,368 records from country-specific and transnational inventories covering 72 countries. Across 23 harmonized fields, registers shared a descriptive core but rarely requested information about appeals, risks, legal bases, or external evaluation. We found that broad schemas often contained substantial missingness, schema similarity showed no significant patterned convergence, and multiple sources covering the same jurisdictions overlapped only selectively. Based on these findings, we synthesize a layered visibility framework that shows how register records reflect disclosure arrangements and why interoperability requires shared concepts, clear definitions, and preserved provenance.

Problem

• AI registers
• transparency
• interoperability
• public-sector AI

Innovation

• interoperability
• transparency
• public-sector AI registers
• layered visibility framework
• disclosure arrangements