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.
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arXiv:2607. 15480v1 Announce Type: new Abstract: As artificial intelligence (AI) systems increasingly impact society, ensuring their ethical and trustworthy deployment has become a global priority.
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:2609.24883v1 Announce Type: new Abstract: Artificial intelligence (AI) registers and inventories aim to make governmental AI visible, but their institutional scope, schemas, and reporting pract...
arXiv:2511. 08639v4 Announce Type: replace-cross Abstract: Existing AI disclosure mandates in scholarship require that AI assistance be reported but leave transparency philosophically unspecified: they fix the duty without explaining what the duty serves.
The paper argues that current model cards are inadequate for governing open‑weight foundation models (OWFMs). By analyzing 500 Hugging Face model cards, it identifies safety gaps in areas such as model heritage, alignment provenance, and observed behaviors. The authors propose a multi‑layered governance framework that combines model cards, acceptable use policies (AUPs), and licenses to create a more comprehensive safety artifact.
The Global Index on Responsible AI 2026 (GIRAI) 2nd Edition refines its predecessor by distinguishing between framework existence and implementation, expanding from three to five thematic areas, and adding granular variables for framework quality. It evaluates responsible AI governance across five dimensions—Inclusion and Diversity, Ethics and Sustainability, Labour and Skills, Trust and Safety, and Use of AI in Public Service—using 38 indicators organized into three pillars: AI Policy, CSO Engagement, and Enabling Conditions, plus a separate Use of Unacceptable Risk AI penalty. Data from 135 country-level researchers and secondary sources are normalized to a 100-point scale, weighted by pillar importance, and used to facilitate systematic cross‑national comparisons for policymakers, civil society, and AI developers.