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Expanding Observability via Human-Machine Cooperation

dc.contributor.authorŠpelda, Petr
dc.contributor.authorStřítecký, Vít
dc.date.accessioned2024-07-10T09:15:44Z
dc.date.available2024-07-10T09:15:44Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/20.500.14178/2553
dc.description.abstractWe ask how to use machine learning to expand observability, which presently depends on human learning that informs conceivability. The issue is engaged by considering the question of correspondence between conceived observability counterfactuals and observable, yet so far unobserved or unconceived, states of affairs. A possible answer lies in importing out of reference frame content which could provide means for conceiving further observability counterfactuals. They allow us to define high-fidelity observability, increasing the level of correspondence in question. To achieve high-fidelity observability, we propose to use generative machine learning models as the providers of the out of reference frame content. From an applied point of view, such a role of generative machine learning models shows an emerging dimension of human-machine cooperation.en
dc.language.isoen
dc.relation.urlhttps://link.springer.com/article/10.1007/s10516-022-09636-0
dc.rightsVydavatel umožňuje vložení autorské verze článku do institucionálního repozitáře.
dc.titleExpanding Observability via Human-Machine Cooperationen
dcterms.accessRightsembargoedAccess
dcterms.licensehttps://www.springernature.com/gp/new-content-item/23214260
dc.date.updated2024-07-10T09:15:44Z
dc.subject.keywordobservabilityen
dc.subject.keywordmachine learningen
dc.subject.keywordconceivabilityen
dc.subject.keywordhuman-machine cooperationen
dc.identifier.eissn1572-8390
dc.relation.fundingReferenceinfo:eu-repo/grantAgreement/MSM//LX22NPO5101
dc.date.embargoStartDate2024-07-10
dc.date.embargoEndDate2024-07-09
dc.type.obd73
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.identifier.doi10.1007/s10516-022-09636-0
dc.identifier.utWos000864311400001
dc.identifier.eidScopus2-s2.0-85139435175
dc.identifier.obd616169
dc.identifier.rivRIV/00216208:11230/22:10448510
dc.subject.rivPrimary50000::50600::50601
dc.subject.rivSecondary60000::60300::60301
dcterms.isPartOf.nameAxiomathes
dcterms.isPartOf.issn1122-1151
dcterms.isPartOf.journalYear2022
dcterms.isPartOf.journalVolume32
dcterms.isPartOf.journalIssueSuppl. 3
uk.faculty.primaryId118
uk.faculty.primaryNameFakulta sociálních vědcs
uk.faculty.primaryNameFaculty of Social Sciencesen
uk.department.primaryId2492
uk.department.primaryNameKatedra bezpečnostních studiícs
uk.department.primaryNameDepartment of Security Studiesen
dc.description.pageRange819-832
dc.type.obdHierarchyCsČLÁNEK V ČASOPISU::článek v časopisu::původní článekcs
dc.type.obdHierarchyEnJOURNAL ARTICLE::journal article::original articleen
dc.type.obdHierarchyCode73::152::206en
uk.displayTitleExpanding Observability via Human-Machine Cooperationen


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