A Global Comparison of Schemas, Transparency, and Interoperability in Public-Sector AI Registers and Inventories
A study of public-sector AI registers and inventories finds missing data and proposes a layered visibility framework for transparency and interoperability.
This paper compares 8,368 public-sector AI records across 72 countries and finds broad schemas have substantial missingness, with limitations in transparency and interoperability, proposing a layered visibility framework.
- Study of public-sector AI registers and inventories for transparency and interoperability
- 8,368 records across 72 countries compared across 23 harmonized fields
- Identifies missingness in register schemas and need for shared concepts and provenance
- Layered visibility framework for register disclosure arrangements
- Vendors
- Microsoft
- Products
- Prompt Guard 2
- Threat actors
- Scattered Spider
- Malware
- LockBit
- Organizations
- 13 organizations
- Countries
- 72
- AI models
- Llama 2GPT-5.1Mistral Large 3
From the original article
1 min read at arxiv.orgArtificial 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.
By Dipto Das · arxiv.org
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