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dc.contributor.authorDierickx, Laurence
dc.contributor.authorLinden, Carl-Gustav
dc.contributor.authorOpdahl, Andreas Lothe
dc.date.accessioned2024-09-11T09:41:16Z
dc.date.available2024-09-11T09:41:16Z
dc.date.created2023-12-04T14:31:54Z
dc.date.issued2023
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11250/3151405
dc.description.abstractLarge language models have enabled the rapid production of misleading or fake narratives, presenting a challenge for direct detection methods. Considering that generative artificial intelligence tools are likely to be used either to inform or to disinform, evaluating the (non)human nature of machine-generated content is questioned, especially regarding the ‘hallucination’ phenomenon, which relates to generated content that does not correspond to real-world input. In this study, we argue that assessing machine-generated content is most reliable when done by humans because doing so involves critical consideration of the meaning of the information and its informative, misinformative or disinformative value, which is related to the accuracy and reliability of the news. To explore human-based judgement methods, we developed the Information Disorder Level (IDL) index, a language-independent metric to evaluate the factuality of machine-generated content. It has been tested on a corpus of forty made-up and actual news stories generated with ChatGPT. For newsrooms using generative AI, results suggest that every piece of machine-generated content should be vetted and post-edited by humans before being published. From a digital media literacy perspective, the IDL index is a valuable tool to understand the limits of generative AI and trigger a reflection on what constitutes the factuality of a reported event.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.urihttps://nordishub.eu/about/
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleThe Information Disorder Level (IDL) Index: A Human-Based Metric to Assess the Factuality of Machine-Generated Contenten_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1007/978-3-031-47896-3_5
dc.identifier.cristin2208599
dc.source.journalLecture Notes in Computer Science (LNCS)en_US
dc.source.pagenumber60-71en_US
dc.identifier.citationLecture Notes in Computer Science (LNCS). 2023, 14397, 60-71.en_US
dc.source.volume14397en_US


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