Acceptance of AI for delegating emotional intelligence: Results from an experiment

dc.contributor.authorAysolmaz, Banu
dc.contributor.authorLeyer, Michael
dc.contributor.authorIren, Deniz
dc.date.accessioned2020-12-24T20:18:52Z
dc.date.available2020-12-24T20:18:52Z
dc.date.issued2021-01-05
dc.description.abstractDetecting emotions of other humans is challenging for us humans. It is however important in many social contexts so that many individuals seek help in this regard. As technology is evolving, more and more AI-based options emerge that promise to detect human emotions and support decision making. We focus on the full delegation of detecting emotions to AI to contribute to our understanding how such AI is perceived and why it is accepted. For this, we conduct an online scenario-based experiment in which participants have the choice to delegate emotion detection to another human in one group and to an AI tool in the other group. Our results show that the delegation rates are higher for a human, but surprisingly high for AI. The results provide insights that should be considered when designing AI-based emotion-detection tools to build trustworthy and accepted designs.
dc.format.extent10 pages
dc.identifier.doihttps://doi.org/10.24251/HICSS.2021.761
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71381
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPromises and Perils of Artificial Intelligence and Machine Learning: Disruption, Adoption, Dehumanisation, Governance, Risk, and Compliance
dc.subjectartificial intelligence
dc.subjectattachment theory
dc.subjectemotion detection
dc.subjectemotions
dc.subjectexperiment
dc.titleAcceptance of AI for delegating emotional intelligence: Results from an experiment
prism.startingpage6307

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