January 25, 2023 Preprint Open Access

Julio Hernandez; Dave Lewis

DCAT Export

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        <foaf:name>Julio Hernandez</foaf:name>
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        <foaf:name>Dave Lewis</foaf:name>
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    <dct:title>Open Requirements Modelling for Compliance and Conformity of Trustworthy AI</dct:title>
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    <dcat:keyword>Trustworthy AI, AI Act, regulation, standards, SC42, semantic web</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2023-01-25</dct:issued>
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    <dct:description>&lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;The many initiatives on trustworthy AI result in a confusing and multipolar landscape that organizations are operating within the fluid and complex international value chains must navigate in pursuing trustworthiness AI. The EU&amp;rsquo;s proposed Draft AI Act will now shift the focus of such organizations toward the normative requirements for regulatory compliance. Understanding the degree to which standards compliance will deliver regulatory compliance for AI remains a complex challenge. This paper offers a simple and repeatable mechanism for extracting and sharing the terms and concepts relevant to normative statements in the legal and 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 will be required to achieve regulatory compliance.&lt;/p&gt;</dct:description>
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