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Human in the loop information extraction increases efficiency and trust

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dc.contributor.authorSchleith, Johannes
dc.contributor.authorHoffmann, Hella
dc.contributor.authorNorkute, Milda
dc.contributor.authorCechmanek, Brian
dc.contributor.editorMarky, Karola
dc.contributor.editorGrünefeld, Uwe
dc.contributor.editorKosch, Thomas
dc.date.accessioned2022-08-30T10:27:42Z
dc.date.available2022-08-30T10:27:42Z
dc.date.issued2022
dc.description.abstractAutomation is often focused on data-centred measures of success, such as accuracy of the automation or efficiency gain of individual automated steps. This case study shows how a human-assisted information extraction system, that keeps the human in the loop throughout the creation of information extraction rules and their application, can outperform less transparent information extraction systems in terms of overall end-to-end time-on-task and perceived trust. We argue that the time gained through automation can be wiped out by the perceived need of end users to review and comprehend results, where the systems seem obscure to them.en
dc.identifier.doi10.18420/muc2022-mci-ws12-249
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39100
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2022 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.subjectInformation Extraction
dc.subjectHuman In The Loop
dc.subjectHuman Centered AI
dc.titleHuman in the loop information extraction increases efficiency and trusten
dc.typeText/Workshop Paper
gi.citation.publisherPlaceBonn
gi.conference.date4.-7. September 2022
gi.conference.locationDarmstadt
gi.conference.sessiontitleMCI-WS12: UCAI 2022: Workshop on User-Centered Artificial Intelligence
gi.document.qualitydigidoc

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