一种基于开放知识图谱的方法，用于映射欧盟《AI法案》与国际标准之间的概念和需求
An Open Knowledge Graph-Based Approach for Mapping Concepts and Requirements between the EU AI Act and International Standards
AI总结：
本文提出一种基于开放知识图谱的可重复机制，将欧盟《AI法案》与ISO等国际标准的法规术语及要求进行映射，以评估标准符合性对监管合规的充分性，识别需进一步发展技术共识的领域。
摘要：
众多关于可信AI的倡议导致了一个令人困惑且多极化的格局，在流动且复杂的国际价值链中运营的组织在追求可信AI时必须应对这种格局。欧盟的《AI法案》现在将使这些组织的关注点转向符合技术要求以实现监管合规，而该法案依赖于协调标准。尽管与该法案要求的高层映射将成为此类协调的一部分，但确定标准符合性在多大程度上能实现对《AI法案》的监管合规，仍然是一个复杂的挑战。该法案与协调标准之间在概念定义及其在要求中的使用方式上的差异和差距，可能会影响跨组织、跨部门和跨应用合规声明的一致性。这可能带来监管不确定性，特别是对于依赖标准符合性而非专有等效物来开发和部署合规高风险AI系统的中小企业和公共部门机构而言。为了应对这一挑战，本文提供了一种简单且可重复的机制，用于将法规和标准（例如《AI法案》和ISO管理体系标准）文本中与规范性声明相关的术语和要求映射到开放知识图谱中。这种表示方法用于评估标准符合性对监管合规的充分性，从而为识别可信AI价值链中需要进一步发展技术共识以实现监管合规的领域提供基础。
英文摘要：
The many initiatives on trustworthy AI result in a confusing and multipolar landscape that organizations operating within the fluid and complex international value chains must navigate in pursuing trustworthy AI. The EU's AI Act will now shift the focus of such organizations toward conformance with the technical requirements for regulatory compliance, for which the Act relies on Harmonized Standards. Though a high-level mapping to the Act's requirements will be part of such harmonization, determining the degree to which standards conformity delivers regulatory compliance with the AI Act remains a complex challenge. Variance and gaps in the definitions of concepts and how they are used in requirements between the Act and harmonized standards may impact the consistency of compliance claims across organizations, sectors, and applications. This may present regulatory uncertainty, especially for SMEs and public sector bodies relying on standards conformance rather than proprietary equivalents for developing and deploying compliant high-risk AI systems. To address this challenge, this paper offers a simple and repeatable mechanism for mapping the terms and requirements relevant to normative statements in regulations and standards, e.g., AI Act and ISO management system standards, texts into open knowledge graphs. This representation is used to assess the adequacy of standards conformance to regulatory compliance and thereby provide a basis for identifying areas where further technical consensus development in trustworthy AI value chains is required to achieve regulatory compliance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
