An Open Knowledge Graph-Based Approach for Mapping Concepts and Requirements between the EU AI Act and International Standards
<|reference_start|>An Open Knowledge Graph-Based Approach for Mapping Concepts and Requirements between the EU AI Act and International Standards: 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.<|reference_end|>
@article{hernandez2024an,
    title={An Open Knowledge Graph-Based Approach for Mapping Concepts and
  Requirements between the EU AI Act and International Standards},
    author={Julio Hernandez, Delaram Golpayegani, Dave Lewis},
    journal={arXiv preprint arXiv:2408.11925},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11925},
    primaryClass={cs.AI cs.CY}
}