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

arXiv: https://arxiv.org/abs/2609.24883 · HTML: https://mlsanity.com/paper/2609.24883/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 66 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 16 of 16)

- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems](https://mlsanity.com/paper/2101.09385/) (2021)
- [A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare](https://mlsanity.com/paper/2107.03487/) (2021)
- [Improving governance outcomes through AI documentation: Bridging theory and practice](https://mlsanity.com/paper/2409.08960/) (2024)
- [Automated Transparency: A Legal and Empirical Analysis of the Digital Services Act Transparency Database](https://mlsanity.com/paper/2404.02894/) (2024)
- [The Neutrality Fallacy: When Algorithmic Fairness Interventions are (Not) Positive Action](https://mlsanity.com/paper/2404.12143/) (2024)
- [The Datafication of Care in Public Homelessness Services](https://mlsanity.com/paper/2502.09043/) (2025)
- [The "Who", "What", and "How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools](https://mlsanity.com/paper/2502.13294/) (2025)
- [Content Moderation on Social Media in the EU: Insights From the DSA Transparency Database](https://mlsanity.com/paper/2312.04431/) (2023)
- [Accountability Capture: How Record-Keeping to Support AI Transparency and Accountability (Re)shapes Algorithmic Oversight](https://mlsanity.com/paper/2510.04609/) (2025)
- [Dutch Comfort: The limits of AI governance through municipal registers](https://mlsanity.com/paper/2109.02944/) (2021)
- [Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration](https://mlsanity.com/paper/2604.21554/) (2026)
- [Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountability](https://mlsanity.com/paper/2604.16106/) (2026)
- ["Is This Not Enough?": Asymmetries in Institutional Accountability and Collective Sensemaking in the Case of Canada's Algorithmic Visa Triage System](https://mlsanity.com/paper/2606.13071/) (2026)

## Abstract (arXiv)

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.