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Authors: Daniela Azevedo ; Axel Legay and Suzanne Kieffer

Affiliation: Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Keyword(s): User Experience, mHealth, Babylon Health, Chatbot, Artificial Intelligence.

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.

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Paper citation in several formats:
Azevedo, D.; Legay, A. and Kieffer, S. (2022). User Reception of Babylon Health’s Chatbot. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - HUCAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 134-141. DOI: 10.5220/0010803000003124

@conference{hucapp22,
author={Daniela Azevedo. and Axel Legay. and Suzanne Kieffer.},
title={User Reception of Babylon Health’s Chatbot},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - HUCAPP},
year={2022},
pages={134-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010803000003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - HUCAPP
TI - User Reception of Babylon Health’s Chatbot
SN - 978-989-758-555-5
IS - 2184-4321
AU - Azevedo, D.
AU - Legay, A.
AU - Kieffer, S.
PY - 2022
SP - 134
EP - 141
DO - 10.5220/0010803000003124
PB - SciTePress