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Bits and Biases

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Resumo:In an AI-infused world, user trust in responses generated by autonomous systems is of critical importance. Building upon the work of Ahn, Kim, and Sung (2022), this study examines the impact of stereotypes attributed to chatbots on user trust using the Stereotype Content Model (SCM), which relies on dimensions like warmth and competence for universal cross-culture social judgment. This research investigates how age-related stereotypes influence user perceptions of anthropomorphic AI, specifically chatbots, and their perceived warmth and competence. We conducted two experiments: Study 1 used AI-generated illustrations to present "young" and "old" chatbot personas, while Study 2 used realistic photos. Participants watched pre-recorded interactions with the chatbot "Dave" and evaluated its warmth and competence on a 9-point Likert scale. Data were collected through Prolific, ensuring a diverse sample. Study 1 found no significant differences in perceptions of warmth and competence between the young and old chatbot personas. However, Study 2 revealed that the younger persona was perceived as warmer than the older one, indicating that the realism of the chatbot's appearance affects stereotype activation. These results underscore the importance of aligning chatbot personas with user expectations to enhance trust and satisfaction.
Autores principais:Macieira, Fernando Jorge Ferreira
Outros Autores:Pinto, Diego Costa; Oliveira, Tiago; Yanaze, Mitsuru Higuchi
Assunto:SCM CASA AI chatbot anthropomorphism SDG 8 - Decent Work and Economic Growth SDG 9 - Industry, Innovation, and Infrastructure
Ano:2025
País:Portugal
Tipo de documento:documento de conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL
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author Macieira, Fernando Jorge Ferreira
author2 Pinto, Diego Costa
Oliveira, Tiago
Yanaze, Mitsuru Higuchi
author2_role author
author
author
author_facet Macieira, Fernando Jorge Ferreira
Pinto, Diego Costa
Oliveira, Tiago
Yanaze, Mitsuru Higuchi
author_role author
contributor_name_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
country_str PT
creators_json_txt [{\"Person.name\":\"Macieira, Fernando Jorge Ferreira\"},{\"Person.name\":\"Pinto, Diego Costa\"},{\"Person.name\":\"Oliveira, Tiago\"},{\"Person.name\":\"Yanaze, Mitsuru Higuchi\"}]
datacite.contributors.contributor.contributorName.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
datacite.creators.creator.creatorName.fl_str_mv Macieira, Fernando Jorge Ferreira
Pinto, Diego Costa
Oliveira, Tiago
Yanaze, Mitsuru Higuchi
datacite.date.Accepted.fl_str_mv 2025-04-01T00:00:00Z
datacite.date.available.fl_str_mv 2025-04-24T21:18:00Z
datacite.date.embargoed.fl_str_mv 2025-04-24T21:18:00Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv SCM
CASA
AI
chatbot
anthropomorphism
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
datacite.titles.title.fl_str_mv Bits and Biases
Exploring perceptions in human-like AI interactions using the Stereotype Content Model
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.creator.none.fl_str_mv Macieira, Fernando Jorge Ferreira
Pinto, Diego Costa
Oliveira, Tiago
Yanaze, Mitsuru Higuchi
dc.date.Accepted.fl_str_mv 2025-04-01T00:00:00Z
dc.date.available.fl_str_mv 2025-04-24T21:18:00Z
dc.date.embargoed.fl_str_mv 2025-04-24T21:18:00Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10362/182608
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv SciTePress - Science and Technology Publications
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv SCM
CASA
AI
chatbot
anthropomorphism
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
dc.title.fl_str_mv Bits and Biases
Exploring perceptions in human-like AI interactions using the Stereotype Content Model
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_c94f
description In an AI-infused world, user trust in responses generated by autonomous systems is of critical importance. Building upon the work of Ahn, Kim, and Sung (2022), this study examines the impact of stereotypes attributed to chatbots on user trust using the Stereotype Content Model (SCM), which relies on dimensions like warmth and competence for universal cross-culture social judgment. This research investigates how age-related stereotypes influence user perceptions of anthropomorphic AI, specifically chatbots, and their perceived warmth and competence. We conducted two experiments: Study 1 used AI-generated illustrations to present "young" and "old" chatbot personas, while Study 2 used realistic photos. Participants watched pre-recorded interactions with the chatbot "Dave" and evaluated its warmth and competence on a 9-point Likert scale. Data were collected through Prolific, ensuring a diverse sample. Study 1 found no significant differences in perceptions of warmth and competence between the young and old chatbot personas. However, Study 2 revealed that the younger persona was perceived as warmer than the older one, indicating that the realism of the chatbot's appearance affects stereotype activation. These results underscore the importance of aligning chatbot personas with user expectations to enhance trust and satisfaction.
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eu_rights_str_mv openAccess
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id run_8d33e06f577f41b150bd22ebdbbb323c
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institution Universidade Nova de Lisboa
instname_str Universidade Nova de Lisboa
language eng
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oai_identifier_str oai:run.unl.pt:10362/182608
organization_str_mv urn:organizationAcronym:unl
person_str_mv Macieira, Fernando Jorge Ferreira
Pinto, Diego Costa
Oliveira, Tiago
Yanaze, Mitsuru Higuchi
publishDate 2025
publisher.none.fl_str_mv SciTePress - Science and Technology Publications
reponame_str Repositório Institucional da UNL
repository_id_str urn:repositoryAcronym:run
service_str_mv urn:repositoryAcronym:run
spelling engSciTePress - Science and Technology PublicationsenIn an AI-infused world, user trust in responses generated by autonomous systems is of critical importance. Building upon the work of Ahn, Kim, and Sung (2022), this study examines the impact of stereotypes attributed to chatbots on user trust using the Stereotype Content Model (SCM), which relies on dimensions like warmth and competence for universal cross-culture social judgment. This research investigates how age-related stereotypes influence user perceptions of anthropomorphic AI, specifically chatbots, and their perceived warmth and competence. We conducted two experiments: Study 1 used AI-generated illustrations to present "young" and "old" chatbot personas, while Study 2 used realistic photos. Participants watched pre-recorded interactions with the chatbot "Dave" and evaluated its warmth and competence on a 9-point Likert scale. Data were collected through Prolific, ensuring a diverse sample. Study 1 found no significant differences in perceptions of warmth and competence between the young and old chatbot personas. However, Study 2 revealed that the younger persona was perceived as warmer than the older one, indicating that the realism of the chatbot's appearance affects stereotype activation. These results underscore the importance of aligning chatbot personas with user expectations to enhance trust and satisfaction.application/pdfenBits and BiasesSubtitleenExploring perceptions in human-like AI interactions using the Stereotype Content ModelMacieira, Fernando Jorge FerreiraPinto, Diego CostaOliveira, TiagoYanaze, Mitsuru HiguchiNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptISBNIsPartOf978-989-758-748-1URNIsPartOfPURE: 106544426URNIsPartOfPURE UUID: 65bc53e2-34fc-45c7-aa35-0611131a193bURNIsPartOfORCID: /0000-0003-4418-9450/work/182885697URNIsPartOfORCID: /0000-0001-6523-0809/work/182886419DOIIsPartOf10.5220/00131927000039562025-04-24T21:18:00Z2025-042025-04-01T00:00:00ZHandlehttp://hdl.handle.net/10362/182608http://purl.org/coar/access_right/c_abf2open accessSCMCASAAIchatbotanthropomorphismSDG 8 - Decent Work and Economic GrowthSDG 9 - Industry, Innovation, and Infrastructure367523 bytesother research producthttp://purl.org/coar/resource_type/c_c94fconference objecthttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/c40c8f52-99a0-4da7-a8a3-2ff2f1db6429/download
spellingShingle Bits and Biases
Macieira, Fernando Jorge Ferreira
SCM
CASA
AI
chatbot
anthropomorphism
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
status SINGLETON
subject.fl_str_mv SCM
CASA
AI
chatbot
anthropomorphism
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
title Bits and Biases
title_full Bits and Biases
title_fullStr Bits and Biases
title_full_unstemmed Bits and Biases
title_short Bits and Biases
title_sort Bits and Biases
topic SCM
CASA
AI
chatbot
anthropomorphism
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
topic_facet SCM
CASA
AI
chatbot
anthropomorphism
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
url http://hdl.handle.net/10362/182608
visible 1