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User Reception of Babylon Health's Chatbot

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Titel: User Reception of Babylon Health's Chatbot
Autoren: Magalhaes Azevedo, Daniela, Legay, Axel, Kieffer, Suzanne, HUCAPP 2022, 6th International Conference on Human Computer Interaction Theory and Applications
Weitere Verfasser: UCL - SSH/ILC/PCOM - Pôle de recherche en communication, UCL - SST/ICTM/INGI - Pôle en ingénierie informatique
Publikationsjahr: 2022
Bestand: DIAL@USL-B (Université Saint-Louis, Bruxelles)
Schlagwörter: User Experience, mHealth, Babylon Health, Chatbot, Artificial Intelligence
Beschreibung: Over the past decade, renewed interest in artificial intelligence systems prompted a proliferation of human-computer studies studies. These studies uncovered several factors impacting users’ appraisal and evaluation of AI systems. One key finding is that users consistently evaluated AI systems performing a given task more harshly than human experts performing the same task. This study aims to uncover another finding: by presenting a mHealth app as either AI or omitting the AI label and asking participants to perform a task, we evaluated whether users still consistently evaluate AI systems more harshly. Moreover, by picking young and well educated participants, we also open new research avenues to be further studied.
Publikationsart: conference object
Sprache: English
Relation: boreal:258299; http://hdl.handle.net/2078.1/258299
DOI: 10.5220/0000156800003124
Verfügbarkeit: http://hdl.handle.net/2078.1/258299
https://doi.org/10.5220/0000156800003124
Rights: info:eu-repo/semantics/openAccess
Dokumentencode: edsbas.E9F21A02
Datenbank: BASE
Beschreibung
Abstract:Over the past decade, renewed interest in artificial intelligence systems prompted a proliferation of human-computer studies studies. These studies uncovered several factors impacting users’ appraisal and evaluation of AI systems. One key finding is that users consistently evaluated AI systems performing a given task more harshly than human experts performing the same task. This study aims to uncover another finding: by presenting a mHealth app as either AI or omitting the AI label and asking participants to perform a task, we evaluated whether users still consistently evaluate AI systems more harshly. Moreover, by picking young and well educated participants, we also open new research avenues to be further studied.
DOI:10.5220/0000156800003124