Human-in-the-loop machine learning: reconceptualizing the role of the user in interactive approaches

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dc.contributor.authorGómez Carmona, Oihane
dc.contributor.authorCasado Mansilla, Diego
dc.contributor.authorLópez de Ipiña González de Artaza, Diego
dc.contributor.authorGarcía-Zubía, Javier
dc.date.accessioned2024-11-15T08:24:45Z
dc.date.available2024-11-15T08:24:45Z
dc.date.issued2024-04
dc.date.updated2024-11-15T08:24:45Z
dc.description.abstractThe rise of intelligent systems and smart spaces has opened up new opportunities for human–machine collaborations. Interactive Machine Learning (IML) contribute to fostering such collaborations. Nonetheless, IML solutions tend to overlook critical factors such as the timing, frequency and workload that drive this interaction and are vital to adapting these systems to users’ goals and engagement. To address this gap, this work explores users’ expectations towards IML solutions in the context of an interactive hydration monitoring system for the workplace, which represents a challenging environment to implement intelligent solutions that can collaborate with individuals. The proposed system involves users in the learning process by providing feedback on the success of detecting their drinking gestures and enabling them to contribute with additional examples of their data. A qualitative study was conducted to evaluate this use case, where participants completed specific tasks with varying levels of involvement. This study provides promising insights into the potential of placing the Human-in-the-Loop (HitL) to adapt and reconceptualize the users’ role in interactive solutions, highlighting the importance of considering human factors in designing more effective and flexible collaborative systems between humans and machines.en
dc.description.sponsorshipWe gratefully acknowledge the support of the Basque Governmentś Department of Education, Spain for the predoctoral funding of one of the authors and the DEUSTEK5 Research Group ( IT1582-22 ). We also acknowledge the Ministry of Economy, Industry and Competitiveness of Spain for IoP , under Grant No. PID2020-119682RB-I00 . This work has been partially supported by the European Commission through the AURORAL project Under Grant No. 101016854en
dc.identifier.citationGómez-Carmona, O., Casado-Mansilla, D., López-de-Ipiña, D., & García-Zubia, J. (2024). Human-in-the-loop machine learning: Reconceptualizing the role of the user in interactive approaches. Internet of Things (Netherlands), 25. https://doi.org/10.1016/J.IOT.2023.101048
dc.identifier.doi10.1016/J.IOT.2023.101048
dc.identifier.issn2542-6605
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1887
dc.language.isoeng
dc.publisherElsevier B.V.
dc.rights© 2023 The Author(s)
dc.subject.otherHuman-in-the-loop
dc.subject.otherIntelligent environments
dc.subject.otherInteractive machine learning
dc.subject.otherInternet of things
dc.subject.otherSmart workplace
dc.titleHuman-in-the-loop machine learning: reconceptualizing the role of the user in interactive approachesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.titleInternet of Things (Netherlands)
oaire.citation.volume25
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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