发表机构
Computational Intelligence and Operations Laboratory (CIOL); Shahjalal University of Science and Technology; Independent University; Hanyang University; University of Oklahoma（计算智能与运筹实验室（CIOL）; 沙贾拉尔科技大学; 独立大学; 汉阳大学; 俄克拉荷马大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出AI治理需在法律基础上补充类ISO的互操作性协议，通过标准化AI“营养标签”实现跨境风险沟通，以解决监管碎片化问题，同时兼顾创新与中小企业需求。
AI 中文摘要
随着人工智能（AI）系统深度融入全球关键基础设施，构建健全治理框架的紧迫性不断提升。然而，当前以辖区特定法律、政策及自愿框架（如《欧盟AI法案》、中国算法治理、美国NIST AI风险管理框架）为主导的治理路径，形成了碎片化的监管格局。本立场论文提出，AI治理的构建不应仅依赖法律，还需依托类ISO的互操作性协议，以实现跨境标准化、机器可读的风险沟通。借鉴GDPR通过ISO 27001、隐私设计等标准落地的成功经验，本文提议开发标准化AI“营养标签”，包含偏见、能耗及数据溯源的统一指标，以促进跨辖区合规。这些标签将降低中小企业（SMEs）的准入门槛、减少冗余监管工作并建立公众信任。针对“标准可能抑制创新”的担忧，本文倡导采用模块化、版本化的协议，使其能随技术变革同步演进。总体而言，本文呼吁从孤岛式法律合规转向可互操作的技术合规，为负责任的AI部署构建共享的全球通用语言。
英文摘要
As Artificial Intelligence (AI) systems become deeply integrated into critical global infrastructure, the urgency for robust governance frameworks has intensified. However, current approaches, led by jurisdiction-specific laws, policies, and voluntary frameworks such as the EU AI Act, China's algorithm governance, and the NIST AI Risk Management Framework in the U.S., create a fragmented regulatory landscape. In this position paper, we argue that \textbf{\textit{AI governance must be built not on laws alone, but on ISO-like interoperability protocols that enable standardized, machine-readable risk communication across borders}}. Drawing on the success of the GDPR, which was operationalized through standards like ISO 27001 and Privacy by Design, we propose the development of standardized AI \textit{nutrition labels} containing unified metrics for bias, energy usage, and data provenance to facilitate cross-jurisdictional compliance. These manifests would lower barriers for small and medium enterprises (SMEs), reduce redundant regulatory efforts, and build public trust. The paper addresses concerns that standards may stifle innovation by advocating for modular, versioned protocols designed to evolve in tandem with technological change. Overall, we call for a shift from siloed legal compliance toward interoperable technical conformance, enabling a shared global language for responsible AI deployment.
CommentsAccepted to ICML 2026 Position Paper Track (Spotlight) (OpenReview: https://openreview.net/forum?id=TE3ceHd4YU)
