The article proposes seven principles for AI cost and compute accounting to address regulatory challenges. It argues that policymakers use cost and compute as proxies for AI capabilities and risks, and that current technical ambiguities create loopholes. The principles aim to reduce gaming, avoid discouraging risk mitigation, and enable consistent implementation across companies and jurisdictions.
arXiv:2607. 02197v1 Announce Type: cross Abstract: The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems.
arXiv:2607. 16224v1 Announce Type: cross Abstract: An international agreement to limit AI development could be crucial to mitigate risks from AI.
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 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:2608. 14562v1 Announce Type: new Abstract: AI governance is shifting from voluntary ethics to enforceable, risk-based regulation, yet cross-jurisdictional divergence creates compliance uncertainty for operators of high-stakes AI.
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 "Operationalising AI Regulatory Sandboxes: Activities, Requirements, and Technical Assessment under the EU AI Act" outlines a detailed framework for implementing AI Regulatory Sandboxes (AIRS) under the EU AI Act. It maps the sandbox lifecycle into 29 activities, distinguishes between a Core AIRS and an Extended AIRS that includes an AI Technical Sandbox (AITS), and derives 15 infrastructural and governance requirements linked to these activities and provider obligations. The authors also introduce the Sandbox Configurator, an open‑source tool to instantiate AITS environments, aiming to provide structured workflows for regulators, robust evaluation methods for experts, and a transparent compliance pathway for AI providers.
The article examines how AI policies implemented by software organisations affect developers, using insights from 19 interviews. It finds that while such policies aim to reduce risks like data leaks and unauthorized use, they can also impede developers if not properly engaged. The authors propose developer‑centric strategies to help managers and decision makers implement effective AI policies.
Governments use laws, institutions, funding programs and nonbinding guidance to shape how AI is developed and used. Comparing these national approaches is difficult. A binding rule and a detailed volu...
The paper introduces a compliance-first AI architecture for regulated finance, treating regulation as an orientation layer rather than a rigid rule set. It uses a regulatory intent matrix and a governed policy compiler to translate regulatory requirements into concrete prohibitions, obligations, and runtime budgets, while maintaining proportional committee activation and supervisory oversight. Evidence and decisions are recorded on a permissioned DAG with deterministic timestamps, enabling replay, provenance checks, and clear attribution of failures, and clause-level legal indexing ensures portability across the DACH region and the EU.
arXiv:2601.04094v4 Announce Type: cross Abstract: Effective regulation of AI is a defining policy challenge, driven by their integration into all aspects of society. To remain responsive to their rap...
arXiv:2608. 05173v1 Announce Type: cross Abstract: As AI capabilities advance, AI systems will pose greater risks to national security and potentially humanity as a whole.