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dc.creatorBorrego Díaz, Joaquínes
dc.creatorGalán Páez, Juanes
dc.date.accessioned2022-06-30T08:30:23Z
dc.date.available2022-06-30T08:30:23Z
dc.date.issued2022
dc.identifier.citationBorrego Díaz, J. y Galán Páez, J. (2022). Knowledge representation for explainable artificial intelligence: modeling foundations from complex systems. Complex and Intelligent Systems, 8 (2), 1579-1601.
dc.identifier.issn2199-4536es
dc.identifier.urihttps://hdl.handle.net/11441/134822
dc.description.abstractAlongside the particular need to explain the behavior of black box artificial intelligence (AI) systems, there is a general need to explain the behavior of any type of AI-based system (the explainable AI, XAI) or complex system that integrates this type of technology, due to the importance of its economic, political or industrial rights impact. The unstoppable development of AI-based applications in sensitive areas has led to what could be seen, from a formal and philosophical point of view, as some sort of crisis in the foundations, for which it is necessary both to provide models of the fundamentals of explainability as well as to discuss the advantages and disadvantages of different proposals. The need for foundations is also linked to the permanent challenge that the notion of explainability represents in Philosophy of Science. The paper aims to elaborate a general theoretical framework to discuss foundational characteristics of explaining, as well as how solutions (events) would be justified (explained). The approach, epistemological in nature, is based on the phenomenological-based approach to complex systems reconstruction (which encompasses complex AI-based systems). The formalized perspective is close to ideas from argumentation and induction (as learning). The soundness and limitations of the approach are addressed from Knowledge representation and reasoning paradigm and, in particular, from Computational Logic point of view. With regard to the latter, the proposal is intertwined with several related notions of explanation coming from the Philosophy of Science.es
dc.description.sponsorshipAgencia Estatal de Investigación PID2019-109152GB-I00/AEI/10.13039/501100011033es
dc.formatapplication/pdfes
dc.format.extent23es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofComplex and Intelligent Systems, 8 (2), 1579-1601.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectComplex systemses
dc.subjectExplainable artificial intelligencees
dc.subjectEpistemological modelinges
dc.subjectFormal Concept Analysises
dc.titleKnowledge representation for explainable artificial intelligence: modeling foundations from complex systemses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificiales
dc.relation.projectIDPID2019-109152GB-I00/AEI/10.13039/501100011033es
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s40747-021-00613-5es
dc.identifier.doi10.1007/s40747-021-00613-5es
dc.contributor.groupUniversidad de Sevilla. TIC-137: Lógica, Computación e Ingeniería del Conocimientoes
dc.journaltitleComplex and Intelligent Systemses
dc.publication.volumen8es
dc.publication.issue2es
dc.publication.initialPage1579es
dc.publication.endPage1601es
dc.contributor.funderAgencia Estatal de Investigación. Españaes

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