A longitudinal approach for understanding algorithm use

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dc.article.numbere2275
dc.catalogadorjca
dc.contributor.authorChacón Hiriart, Álvaro Marcelo
dc.contributor.authorKausel, Edgar E.
dc.contributor.authorReyes Torres Tomas Hernan
dc.date.accessioned2023-07-11T14:28:37Z
dc.date.available2023-07-11T14:28:37Z
dc.date.issued2022
dc.description.abstractResearch suggests that algorithms-based on artificial intelligence or linear regression models-make better predictions than humans in a wide range of domains. Several studies have examined the degree to which people use algorithms. However, these studies have been mostly cross-sectional and thus have failed to address the dynamic nature of algorithm use. In the present paper, we examined algorithm use with a novel longitudinal approach outside the lab. Specifically, we conducted two ecological momentary assessment studies in which 401 participants made financial predictions for 18 days in two tasks. Relying on the judge-advisor system framework, we examined how time interacted with advice source (human vs. algorithm) and advisor accuracy to predict advice taking. Our results showed that when the advice was inaccurate, people tended to use algorithm advice less than human advice across the period studied. Inaccurate algorithms were penalized logarithmically; the effect was initially strong but tended to fade over time. This suggests that first impressions are crucial and produce significant changes in advice taking at the beginning of the interaction, which later tends to stabilize as days go by. Therefore, inaccurate algorithms are more likely to accrue a negative reputation than inaccurate humans, even when having the same level of performance.
dc.description.funderFondo Nacional de Desarrollo Cientifico y Tecnologico
dc.fechaingreso.objetodigital2023-07-11
dc.format.extent15 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1002/bdm.2275
dc.identifier.eissn1099-0771
dc.identifier.issn0894-3257
dc.identifier.scopusidSCOPUS_ID:85123488500
dc.identifier.urihttps://doi.org/10.1002/bdm.2275
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/74159
dc.identifier.wosidWOS:000746449600001
dc.information.autorucEscuela de Ingeniería; Reyes Torres Tomas Hernan; S/I; 131463
dc.information.autorucEscuela de Ingeniería; Chacón Hiriart, Álvaro Marcelo; S/I; 3908
dc.information.autorucEscuela de Psicología; Kausel, Edgar E. ; S/I; 685
dc.issue.numero4
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherWILEY
dc.revistaJournal of Behavioral Decision Making
dc.rightsacceso restringido
dc.subjectAdvice
dc.subjectAlgorithm appreciation
dc.subjectAlgorithm aversion
dc.subjectAlgorithms
dc.subjectDecision making
dc.subjectForecasting
dc.subjectDecision-making
dc.subjectMultimodel inference
dc.subjectModel selection
dc.subjectTrust
dc.subject.ddc600
dc.subject.deweyTecnologíaes_ES
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleA longitudinal approach for understanding algorithm use
dc.typeartículo
dc.volumen35
sipa.codpersvinculados131463
sipa.codpersvinculados3908
sipa.codpersvinculados685
sipa.indexWoS
sipa.trazabilidadORCID;2023-07-10
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