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dc.contributor.authorOpdahl, Andreas Lothe
dc.contributor.authorTessem, Bjørnar
dc.contributor.authorDang Nguyen, Duc Tien
dc.contributor.authorMotta, Enrico
dc.contributor.authorSetty, Vinay
dc.contributor.authorThrondsen, Eivind
dc.contributor.authorTverberg, Are
dc.contributor.authorTrattner, Christoph
dc.date.accessioned2023-07-05T06:26:49Z
dc.date.available2023-07-05T06:26:49Z
dc.date.created2023-05-15T22:56:56Z
dc.date.issued2023
dc.identifier.issn0169-023X
dc.identifier.urihttps://hdl.handle.net/11250/3075970
dc.description.abstractQuality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.urihttps://doi.org/10.1016/j.datak.2023.102182
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleTrustworthy journalism through AIen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 the authorsen_US
dc.source.articlenumber102182en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.1016/j.datak.2023.102182
dc.identifier.cristin2147698
dc.source.journalData & Knowledge Engineeringen_US
dc.relation.projectNorges forskningsråd: 309339en_US
dc.relation.projectNorges forskningsråd: 275872en_US
dc.identifier.citationData & Knowledge Engineering. 2023, 146, 102182.en_US
dc.source.volume146en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal