发表机构
Cornell University; University of Toronto（康奈尔大学; 多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究比较72国8,368条AI登记记录，发现模式缺乏互操作性和透明度，提出分层可见性框架以促进共享概念与来源保留。
AI 中文摘要
人工智能（AI）登记册和清单旨在使政府AI可见，但其机构范围、模式和报告实践构建了公共部门AI的不同表征。我们比较了来自国家级和跨国清单的8,368条记录，覆盖72个国家。在23个统一字段中，登记册共享一个描述性核心，但很少要求关于申诉、风险、法律依据或外部评估的信息。我们发现，宽泛的模式往往包含大量缺失数据，模式相似性未显示出显著的规律性趋同，且覆盖相同管辖区的多个来源仅选择性重叠。基于这些发现，我们综合了一个分层可见性框架，该框架展示了登记记录如何反映披露安排，以及为何互操作性需要共享概念、清晰定义和保留来源信息。
英文摘要
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.
