arXiv:2607. 25368v1 Announce Type: new Abstract: The intersection of artificial intelligence adoption, cybersecurity governance, and public sector institutional constraints has not been examined as a unified analytical problem in the existing literature.
arXiv:2607. 26069v1 Announce Type: cross Abstract: As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI security readiness continues to widen.
arXiv:2605. 16281v2 Announce Type: replace-cross Abstract: Post-deployment accountability has become central to AI governance, yet little empirical evidence shows whether monitoring, incident reporting, and impact assessment obligations are visible when AI systems fail.
arXiv:2606. 26117v1 Announce Type: cross Abstract: This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditions of increasing regulatory expansion and technological complexity, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems.
arXiv:2608. 12352v1 Announce Type: cross Abstract: AI governance frameworks can be known, used, and implemented in form without becoming governance in practice.
The study evaluates 684 U.S. federal AI governance documents for how they address 14 sectors and 24 AI risks, measuring both breadth and depth of coverage. It finds that risks related to robustness, system security, and governance are more frequently and substantively discussed than socioeconomic, environmental, and emerging risks, and that sectors such as public administration, national security, information, and scientific services receive higher coverage than finance and healthcare. By comparing these coverage patterns with expert vulnerability assessments, the authors identify potential gaps in AI governance that could inform future policy and industry decisions.
arXiv:2607. 25648v1 Announce Type: cross Abstract: Public services face growing pressure to adopt artificial intelligence (AI) to close the gap between rising demand and falling resources.
The paper "Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement" highlights a gap in enterprise AI governance, where 78% of organizations lack auditable evidence of policy enforcement. It introduces AGIL, a five-layer adaptive governance architecture that uses machine learning for real-time detection, risk classification, sub-100ms policy enforcement, continuous attestation, and policy evolution. The authors argue that the failure is organizational and architectural, not technical, and call for future empirical validation of AGIL.
arXiv:2606. 12423v1 Announce Type: cross Abstract: The rapid integration of artificial intelligence (AI) into critical infrastructure including healthcare, finance, energy, and defense, offers transformative benefits but also conflicts with evolving regulatory and governance frameworks.
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.
arXiv:2609.21192v1 Announce Type: new Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate,...
The paper proposes AI Deployment Accountability Engineering (ADAE), a new subdiscipline focused on establishing measurable, continuous, and actionable accountability for AI systems once they are deployed. ADAE treats accountability as a deployment-layer property, aiming to ensure systems remain within acceptable risk limits, identify failure contexts, attribute failures across technical and human components, and translate technical failures into downstream consequences. The authors outline a research agenda built around four pillars—structured discovery of context-dependent failure modes, privacy-preserving accountability measurement, system-level risk analysis for agentic AI, and translation of technical failures into operational and institutional risks—to support timely intervention in safety-critical socio-technical environments.
arXiv:2608. 15417v1 Announce Type: cross Abstract: Governments use laws, institutions, funding programs and nonbinding guidance to shape how AI is developed and used.