Interoperability in AI Safety Governance: Ethics, Regulations, and Standards

• URL: http://arxiv.org/abs/2601.06153v1
• Date: Tue, 06 Jan 2026 05:39:59 GMT
• Title: Interoperability in AI Safety Governance: Ethics, Regulations, and Standards
• Authors: Yik Chan Chin, David A. Raho, Hag-Min Kim, Chunli Bi, James Ong, Jingbo Huang, Serge Stinckwich,
• Abstract summary: This policy report draws on country studies from China, South Korea, Singapore, and the United Kingdom.<n>It identifies effective tools and key barriers to interoperability in AI safety governance.<n>It offers practical recommendations to support a globally informed yet locally grounded governance ecosystem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This policy report draws on country studies from China, South Korea, Singapore, and the United Kingdom to identify effective tools and key barriers to interoperability in AI safety governance. It offers practical recommendations to support a globally informed yet locally grounded governance ecosystem. Interoperability is a central goal of AI governance, vital for reducing risks, fostering innovation, enhancing competitiveness, promoting standardization, and building public trust. However, structural gaps such as fragmented regulations and lack of global coordination, and conceptual gaps, including limited Global South engagement, continue to hinder progress. Focusing on three high-stakes domains - autonomous vehicles, education, and cross-border data flows - the report compares ethical, legal, and technical frameworks across the four countries. It identifies areas of convergence, divergence, and potential alignment, offering policy recommendations that support the development of interoperability mechanisms aligned with the Global Digital Compact and relevant UN resolutions. The analysis covers seven components: objectives, regulators, ethics, binding measures, targeted frameworks, technical standards, and key risks.

Related papers

• Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
  We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
  arXiv  Detail & Related papers  (2025-08-12T09:42:56Z)
• Trustworthiness of Legal Considerations for the Use of LLMs in Education [0.0]
  This paper offers a comparative analysis of AI-related regulatory and ethical frameworks across key global regions.<n>It maps how core trustworthiness principles, such as transparency, fairness, accountability, data privacy, and human oversight are embedded in regional legislation and AI governance structures.<n>The paper contributes practical guidance for building legally sound, ethically grounded, and culturally sensitive AI systems in education.
  arXiv  Detail & Related papers  (2025-08-05T07:44:33Z)
• AI Governance in the GCC States: A Comparative Analysis of National AI Strategies [0.0]
  Gulf Cooperation Council (GCC) states increasingly adopt Artificial Intelligence (AI) to drive economic diversification and enhance services.<n>This paper investigates the evolving AI governance landscape across the six GCC nations, the United Arab Emirates, Saudi Arabia, Qatar, Oman, Bahrain, and Kuwait.<n>Findings highlight a "soft regulation" approach that emphasizes national strategies and ethical principles rather than binding regulations.
  arXiv  Detail & Related papers  (2025-05-04T16:25:52Z)
• Towards Adaptive AI Governance: Comparative Insights from the U.S., EU, and Asia [0.0]
  This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia.<n>It focuses on three key dimensions: generative AI, ethical oversight, and industrial applications.<n>The US prioritizes market-driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk-based framework emphasizing ethical safeguards, and Asia employs state-guided AI strategies that balance rapid deployment with regulatory oversight.
  arXiv  Detail & Related papers  (2025-04-01T11:05:47Z)
• Between Innovation and Oversight: A Cross-Regional Study of AI Risk Management Frameworks in the EU, U.S., UK, and China [0.0]
  This paper conducts a comparative analysis of AI risk management strategies across the European Union, United States, United Kingdom (UK), and China.<n>The findings show that the EU implements a structured, risk-based framework that prioritizes transparency and conformity assessments.<n>The U.S. uses a decentralized, sector-specific regulations that promote innovation but may lead to fragmented enforcement.
  arXiv  Detail & Related papers  (2025-02-25T18:52:17Z)
• On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective [377.2483044466149]
  Generative Foundation Models (GenFMs) have emerged as transformative tools.<n>Their widespread adoption raises critical concerns regarding trustworthiness across dimensions.<n>This paper presents a comprehensive framework to address these challenges through three key contributions.
  arXiv  Detail & Related papers  (2025-02-20T06:20:36Z)
• AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
  This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
  arXiv  Detail & Related papers  (2025-02-19T05:58:52Z)
• AI Risk Management Should Incorporate Both Safety and Security [185.68738503122114]
  We argue that stakeholders in AI risk management should be aware of the nuances, synergies, and interplay between safety and security. We introduce a unified reference framework to clarify the differences and interplay between AI safety and AI security.
  arXiv  Detail & Related papers  (2024-05-29T21:00:47Z)
• AI For Global Climate Cooperation 2023 Competition Proceedings [77.07135605362795]
  No global authority can ensure compliance with international climate agreements. RICE-N supports modeling regional decision-making using AI agents. The IAM then models the climate-economic impact of those decisions into the future.
  arXiv  Detail & Related papers  (2023-07-10T20:05:42Z)
• International Institutions for Advanced AI [47.449762587672986]
  International institutions may have an important role to play in ensuring advanced AI systems benefit humanity. This paper identifies a set of governance functions that could be performed at an international level to address these challenges. It groups these functions into four institutional models that exhibit internal synergies and have precedents in existing organizations.
  arXiv  Detail & Related papers  (2023-07-10T16:55:55Z)
• Bridging the Global Divide in AI Regulation: A Proposal for a Contextual, Coherent, and Commensurable Framework [0.9622882291833615]
  This paper proposes an alternative contextual, coherent, and commensurable (3C) framework for regulating artificial intelligence (AI) To ensure contextuality, the framework bifurcates the AI life cycle into two phases: learning and deployment for specific tasks, instead of defining foundation or general-purpose models. To ensure commensurability, the framework promotes the adoption of international standards for measuring and mitigating risks.
  arXiv  Detail & Related papers  (2023-03-20T15:23:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.