Artificial intelligence (AI) registers and inventories aim to make governmental AI visible, but their institutional scope, schemas, and reporting practices construct different representations of publi...
arXiv:2606. 30652v1 Announce Type: cross Abstract: Transparency is increasingly mandated for public-sector AI systems, with organisations required to publish statements describing their AI use and oversight arrangements.
The paper argues that AI governance should rely on ISO-like interoperability protocols rather than solely on jurisdiction-specific laws. It proposes standardized AI nutrition labels that include metrics for bias, energy usage, and data provenance to enable machine‑readable risk communication across borders. These protocols aim to reduce regulatory fragmentation, lower barriers for SMEs, and build public trust while allowing modular evolution with technology.
arXiv:2606. 00621v1 Announce Type: cross Abstract: Generative artificial intelligence has fundamentally changed how content is now produced.
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.
arXiv:2608. 19278v1 Announce Type: cross Abstract: The most capable general-purpose AI (GPAI) models are mostly built in two jurisdictions, the United States and China, but the risks they carry land globally.
arXiv:2606. 28789v1 Announce Type: cross Abstract: Artificial intelligence reaches the land registry not as another tool but as a value chain that turns data into intelligence and intelligence into economic value.
arXiv:2606. 28331v1 Announce Type: cross Abstract: The widespread deployment of generative artificial intelligence (AI) models has raised serious concerns about the proliferation of AI-generated content.
The paper introduces a reusable Semantic Web framework that aggregates fragmented evidence needed for Fundamental Rights Impact Assessments under the EU AI Act, focusing on high‑risk public sector categories such as employment and worker management and access to essential public services. A curated 150‑record corpus is annotated across four axes and serialized into a SPARQL‑queryable knowledge graph of 1,351 RDF triples, enabling five demonstration scenarios that retrieve 103 records (68.7% coverage). Evaluation against a 69‑record gold standard shows that LLM‑assisted classification in the employment domain yields a low κ of 0.045, highlighting challenges in automated fairness‑related evidence retrieval, while all artefacts are released openly for regulators, authorities, and SMEs.
The paper introduces LAAF, a Layered Accountability Architecture Framework for Large Language Model (LLM) applications, developed through a systematic review of 122 primary studies and 12 regulatory documents. It identifies five accountability dimensions and four families of mechanisms—technical controls, human oversight, organisational governance, and documentation/traceability—evaluated across maturity levels. The framework maps onto major standards such as the EU AI Act, NIST AI RMF, ISO/IEC 42001, and sectoral guidance, highlighting gaps in human oversight, accountability metrics, disciplinary alignment, and empirical validation.
arXiv:2608. 15417v1 Announce Type: cross Abstract: Governments use laws, institutions, funding programs and nonbinding guidance to shape how AI is developed and used.
The paper presents LAAF, a Layered Accountability Architecture Framework for Large Language Model (LLM) applications, developed through a systematic review of 122 primary studies and 12 regulatory documents. It identifies five dimensions of accountability and four families of mechanisms—technical controls, human oversight, organisational governance, and documentation/traceability—each assessed for maturity. The framework is mapped onto major regulatory standards (EU AI Act, NIST AI RMF, ISO/IEC 42001) and highlights persistent gaps such as under‑specified human oversight and lack of shared accountability metrics.