Context Changes and the Performance of a Learning Human-in-the-loop System: A Case Study of Automatic Speech Recognition Use in Medical Transcription

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorMucha, Tomaszen_US
dc.contributor.authorSeppälä, Janeen_US
dc.contributor.authorPuraskivi, Henriken_US
dc.contributor.departmentDepartment of Industrial Engineering and Managementen
dc.contributor.editorBui, Tung X.en_US
dc.contributor.organizationInscriptaen_US
dc.date.accessioned2023-01-18T09:22:46Z
dc.date.available2023-01-18T09:22:46Z
dc.date.issued2023-01en_US
dc.description.abstractThe paper presents how organizational practices enable the improvement and maintenance of task performance in a learning human-in-the-loop system exposed to a wide range of context changes. We investigate how the case company tripled the efficiency of medical transcribers by leveraging its machine learning-based automatic speech recognition technology. We find that the focal system operated across stable, drifting, and jumping contexts. Despite changes, it continued to improve or maintained performance thanks to two sets of organizational practices aligning it with the context: extending and refining. This paper makes two key contributions: It shows the importance of considering context changes in the design and operation of learning human-in-the-loop systems. Our empirical findings help with resolving some contradictory outcomes of the recent conceptual work. Secondly, we show that context alignment practices are situated at the sociotechnical system level and, thus, are not just technical solution nor can be detached from social elements.en
dc.description.versionPeer revieweden
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMucha, T, Seppälä, J & Puraskivi, H 2023, Context Changes and the Performance of a Learning Human-in-the-loop System: A Case Study of Automatic Speech Recognition Use in Medical Transcription . in T X Bui (ed.), Proceedings of the 56th Hawaii International Conference on System Sciences . Hawaii International Conference on System Sciences, pp. 3121-3130, Annual Hawaii International Conference on System Sciences, Maui, Hawaii, United States, 03/01/2023 . < https://hdl.handle.net/10125/103014 >en
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.otherPURE UUID: 5d3a04c9-a5f2-488c-810c-056da42d0cb8en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/5d3a04c9-a5f2-488c-810c-056da42d0cb8en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85152130682&partnerID=8YFLogxKen_US
dc.identifier.otherPURE LINK: https://hdl.handle.net/10125/103014en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/97552254/Context_Changes_and_the_Performance_of_a_Learning_Human_in_the_loop_System.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/118859
dc.identifier.urnURN:NBN:fi:aalto-202301181215
dc.language.isoenen
dc.relation.ispartofAnnual Hawaii International Conference on System Sciencesen
dc.relation.ispartofseriesProceedings of the 56th Hawaii International Conference on System Sciencesen
dc.rightsopenAccessen
dc.subject.keywordhuman-in-the-loopen_US
dc.subject.keywordmachine learningen_US
dc.subject.keywordartificial intelligenceen_US
dc.subject.keywordtask performanceen_US
dc.titleContext Changes and the Performance of a Learning Human-in-the-loop System: A Case Study of Automatic Speech Recognition Use in Medical Transcriptionen
dc.typeA4 Artikkeli konferenssijulkaisussafi
dc.type.versionpublishedVersion
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