Publikacja:

No Algorithm Aversion in Improving AI

Data

2025
Artykuł
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cris.virtualsource.journalancec5e604c2-f6bd-4f19-914c-e01c8ff3c6c3
dc.abstract.enAlgorithm aversion is the tendency to avoid using algorithms or AI systems. Most of the studies only present participants with the most recent performance of humans or AI. Through a series of experiments, involving N=905 participants, we investigated how people evaluate AI versus human performance, focusing on consistent high-quality output versus improvement over time. Our findings reveal that the preference for humans over AI significantly decreases when both demonstrate improvement. We observe this in the creative domain of tattoo design and other non-creative domains, such as law, logistics, and sales. Emphasizing AI’s improvement could effectively reduce algorithm aversion and increase AI acceptance. Our research contributes to understanding AI acceptance in domains, even those traditionally dominated by human creativity, offering insights for implementing AI systems in various fields.
dc.abstract.plAlgorithm aversion is the tendency to avoid using algorithms or AI systems. Most of the studies only present participants with the most recent performance of humans or AI. Through a series of experiments, involving N=905 participants, we investigated how people evaluate AI versus human performance, focusing on consistent high-quality output versus improvement over time. Our findings reveal that the preference for humans over AI significantly decreases when both demonstrate improvement. We observe this in the creative domain of tattoo design and other non-creative domains, such as law, logistics, and sales. Emphasizing AI’s improvement could effectively reduce algorithm aversion and increase AI acceptance. Our research contributes to understanding AI acceptance in domains, even those traditionally dominated by human creativity, offering insights for implementing AI systems in various fields.
dc.contributor.authorWojciech Milczarski
dc.contributor.authorAnna Borkowska
dc.contributor.authorEmilia Biesiada
dc.contributor.authorLaura Russak
dc.contributor.authorMichał Białek
dc.date.accessioned2025-09-23T14:41:51Z
dc.date.available2025-09-23T14:41:51Z
dc.date.issued2025
dc.description.abstract
dc.description.issue1
dc.description.volume37
dc.identifier.affiliationInstitute of English Studies, The Faculty of Languages, Literatures, and Cultures, University of Wrocław
dc.identifier.affiliationInstitute of Psychology, The Faculty of Historical and Pedagogical Sciences, University of Wrocław
dc.identifier.affiliationInstitute of Psychology, The Faculty of Historical and Pedagogical Sciences, University of Wrocław
dc.identifier.affiliationInstitute of Psychology, The Faculty of Historical and Pedagogical Sciences, University of Wrocław
dc.identifier.affiliationInstitute of Psychology, The Faculty of Historical and Pedagogical Sciences, University of Wrocław
dc.identifier.doi10.7206/cid.3071-7973.9
dc.identifier.orcid0000-0002-8215-5190
dc.identifier.orcid0000-0001-5428-0974
dc.identifier.orcid0000-0002-0113-7290
dc.identifier.orcid0000-0002-8177-5857
dc.identifier.orcid0000-0002-5062-5733
dc.identifier.urihttps://repozytorium.kozminski.edu.pl/handle/item/3734
dc.languageen
dc.publisherKozminski University
dc.relation.ispartofCollective and Individual Decisions
dc.relation.issn3071-7973
dc.relation.pages57–80
dc.subject.enArtificial intelligence
dc.subject.encreativity
dc.subject.entattoo design
dc.subject.enperformance evaluation
dc.subject.enalgorithm aversion
dc.subject.plArtificial intelligence
dc.subject.plcreativity
dc.subject.pltattoo design
dc.subject.plperformance evaluation
dc.subject.plalgorithm aversion
dc.subtypeOriginal
dc.title

No Algorithm Aversion in Improving AI

dc.typeArticle
dspace.entity.typePublication
oaire.citation.volume37