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dc.contributor.authorJha, Ashish
dc.date.accessioned2022-11-16T12:19:55Z
dc.date.available2022-11-16T12:19:55Z
dc.date.issued2022
dc.date.submitted2022en
dc.identifier.citationDmitry Romanov, Valentin Molokanov, Nikolai Kazantsev, Ashish Kumar Jha, Removing order effects from human-classified datasets: A machine learning method to improve decision making systems, Decision Support Systems, 2022en
dc.identifier.otherY
dc.identifier.urihttp://hdl.handle.net/2262/101558
dc.descriptionPUBLISHEDen
dc.description.abstractAlthough recent developments in Artificial Intelligence (AI) and machine learning (ML) aim to enhance the fairness and transparency of decision-making systems, research has found that neural networks (or other similar AI techniques) are still effected by human cognitive biases due to the training datasets. In this study, we focus on order effects, i.e., when the input of information impacts human perception and the decisions resulting from this information. We propose the Order Effect Removal Method (OERM) for handling the order effect which leads to bias and for helping organizations remove these biases from their training datasets and, therefore, from automated decision-making systems. Using design science principles to theoretically create, test, and validate the method, we can eliminate the order bias even in basic classification systems. Furthermore, the method can be applied in a multidisciplinary context, where an AI-based algorithm substitutes for manual work.en
dc.language.isoenen
dc.relation.ispartofseriesDecision Support Systems;
dc.rightsYen
dc.subjectOrder effecten
dc.subjectMachine learningen
dc.subjectArtificial intelligenceen
dc.subjectDecision-makingen
dc.subjectInformation Systems (IS) researchen
dc.titleRemoving order effects from human-classified datasets: A machine learning method to improve decision making systemsen
dc.typeJournal Articleen
dc.contributor.sponsorScience Foundation Ireland (SFI)en
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/akjha
dc.identifier.rssinternalid248085
dc.rights.ecaccessrightsopenAccess
dc.contributor.sponsorGrantNumber13/RC/2106_P2en
dc.subject.TCDThemeDigital Humanitiesen
dc.identifier.orcid_id0000-0002-5450-9983


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