Do stakeholder needs differ? - Designing stakeholder-tailored Explainable Artificial Intelligence (XAI) interfaces

Dados Bibliográficos

ID21642436
AutoresMinjung Kim (0000-0002-1403-8961, Yonsei University), Saebyeol Kim (Yonsei University), Jinwoo Kim (0000-0002-2237-4965), Jin-Woo Kim (0000-0003-0186-5834, Yonsei University), Tae-Jin Song (0000-0002-9937-762X), Yuyoung Kim (0000-0003-2309-8058, Yonsei University, autor correspondente)
Ano2024
Volume181
Páginas103160
Data de publicação2024-01-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoInternational Journal of Human-Computer Studies (JOURNAL)
Identificadores do periódicoISSN: 1071-5819 • E-ISSN: 1095-9300
EditoraElsevier BV (PUBLISHER)
DOI10.1016/j.ijhcs.2023.103160
OpenAlexW4386989268
IdiomaEN
Citações recebidas4
Referências citadas40

Explainable AI (XAI) is increasingly being used in the healthcare domain. In health management, clinicians and patients are critical stakeholders, requiring tailored XAI explanations based on their unique needs. Our study investigates the differences in explanation needs between clinicians and patients and designs corresponding explanation interfaces for each group. Using a scenario-based approach, we assessed stakeholder-tailored needs, analyzed differences, and designed interfaces using theoretical frameworks. The results demonstrate diverse stakeholder motivations for seeking explanations, leading to varied requirements. The designed interfaces effectively address these requirements, as validated by the preference selection and qualitative feedback from clinicians and patients. Their suggestions provide design insights and highlight the divergent needs of these stakeholder groups. This study contributes practical and theoretical implications to XAI research, emphasizing the importance of understanding diverse stakeholder needs and incorporating relevant theoretical concepts into user-centered interface design

Business · Knowledge management · Management science · Political science · Preference · Process management · Public relations · Stakeholder · Stakeholder analysis · Artificial Intelligence in Healthcare and Education · Computer Science · Engineering · Explainable Artificial Intelligence (XAI · Machine Learning in Healthcare