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dc.contributor.authorHorsch, Martin Thomas
dc.contributor.authorSchembera, Björn
dc.contributor.authorPreisig, Heinz Adolf
dc.date.accessioned2023-09-01T05:28:43Z
dc.date.available2023-09-01T05:28:43Z
dc.date.created2023-08-03T22:42:26Z
dc.date.issued2023
dc.identifier.isbn9798350328929
dc.identifier.urihttps://hdl.handle.net/11250/3086748
dc.description.abstractSecurity critical AI applications require a standardized and interoperable data and metadata documentation that makes the source data explainable-AI ready (XAIR). Within the domain of materials modelling and characterization, European initiatives have proposed a series of metadata standards and procedural recommendations that were accepted as CEN workshop agreements (CWAs): CWA 17284 MODA, CWA 17815 CHADA, and CWA 17960 ModGra. It is discussed how these standards have been ontologized, and gaps are identified as regards the epistemic grounding metadata, i.e., an annotation of data and claims by something that substantiates whether, why, and to what extent they are indeed knowledge and can be relied upon.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartof2023 3rd International Conference on Applied Artificial Intelligence (ICAPAI)
dc.titleEuropean standardization efforts from FAIR toward explainable-AI-ready data documentation in materials modellingen_US
dc.title.alternativeEuropean standardization efforts from FAIR toward explainable-AI-ready data documentation in materials modellingen_US
dc.typeChapteren_US
dc.description.versionsubmittedVersionen_US
dc.identifier.doi10.1109/ICAPAI58366.2023.10193944
dc.identifier.cristin2164820
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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