Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems
As artificial intelligence systems gain traction, their trustworthiness becomes paramount to harness their benefits and mitigate risks. This study underscores the pressing need for an expectation management framework to align stakeholder anticipations before any system-related activities, such as da...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024045936 |
| _version_ | 1797232842783064064 |
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| author | Marjorie Kinney Maria Anastasiadou Mijail Naranjo-Zolotov Vitor Santos |
| author_facet | Marjorie Kinney Maria Anastasiadou Mijail Naranjo-Zolotov Vitor Santos |
| author_sort | Marjorie Kinney |
| collection | DOAJ |
| description | As artificial intelligence systems gain traction, their trustworthiness becomes paramount to harness their benefits and mitigate risks. This study underscores the pressing need for an expectation management framework to align stakeholder anticipations before any system-related activities, such as data collection, modeling, or implementation. To this end, we introduce a comprehensive framework tailored to capture end-user expectations specifically for trustworthy artificial intelligence systems. To ensure its relevance and robustness, we validated the framework via semi-structured interviews, encompassing questions rooted in the framework's constructs and principles. These interviews engaged fourteen diverse end users across the healthcare and education sectors, including physicians, teachers, and students. Through a meticulous qualitative analysis of the interview transcripts, we unearthed pivotal themes and discerned varying perspectives among the interviewee groups. Ultimately, our framework stands as a pivotal tool, paving the way for in-depth discussions about user expectations, illuminating the significance of various system attributes, and spotlighting potential challenges that might jeopardize the system's efficacy. |
| first_indexed | 2024-04-24T16:06:42Z |
| format | Article |
| id | doaj.art-9cdd6195ceac4e99961e7d284445a13f |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T16:06:42Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-9cdd6195ceac4e99961e7d284445a13f2024-04-01T04:04:22ZengElsevierHeliyon2405-84402024-04-01107e28562Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systemsMarjorie Kinney0Maria Anastasiadou1Mijail Naranjo-Zolotov2Vitor Santos3NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisboa, PortugalNOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisboa, PortugalCorresponding author.; NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisboa, PortugalNOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisboa, PortugalAs artificial intelligence systems gain traction, their trustworthiness becomes paramount to harness their benefits and mitigate risks. This study underscores the pressing need for an expectation management framework to align stakeholder anticipations before any system-related activities, such as data collection, modeling, or implementation. To this end, we introduce a comprehensive framework tailored to capture end-user expectations specifically for trustworthy artificial intelligence systems. To ensure its relevance and robustness, we validated the framework via semi-structured interviews, encompassing questions rooted in the framework's constructs and principles. These interviews engaged fourteen diverse end users across the healthcare and education sectors, including physicians, teachers, and students. Through a meticulous qualitative analysis of the interview transcripts, we unearthed pivotal themes and discerned varying perspectives among the interviewee groups. Ultimately, our framework stands as a pivotal tool, paving the way for in-depth discussions about user expectations, illuminating the significance of various system attributes, and spotlighting potential challenges that might jeopardize the system's efficacy.http://www.sciencedirect.com/science/article/pii/S2405844024045936Expectation managementArtificial intelligenceMachine learningTrustworthy AIExplainable AIAI development process |
| spellingShingle | Marjorie Kinney Maria Anastasiadou Mijail Naranjo-Zolotov Vitor Santos Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems Heliyon Expectation management Artificial intelligence Machine learning Trustworthy AI Explainable AI AI development process |
| title | Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems |
| title_full | Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems |
| title_fullStr | Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems |
| title_full_unstemmed | Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems |
| title_short | Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems |
| title_sort | expectation management in ai a framework for understanding stakeholder trust and acceptance of artificial intelligence systems |
| topic | Expectation management Artificial intelligence Machine learning Trustworthy AI Explainable AI AI development process |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024045936 |
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