TY - JOUR
T1 - Transparent-AI Blueprint
T2 - Developing a Conceptual Tool to Support the Design of Transparent AI Agents
AU - Zhou, Zhibin
AU - Li, Zhuoshu
AU - Zhang, Yuyang
AU - Sun, Lingyun
N1 - Funding Information:
This is paper is funded by National Key R&D Program of China [2018AAA0100703], the Provincial Key Research and Development Plan of Zhejiang Province [No. 2019C03137], and the National Natural Science Foundation of China [No. 62006208 and No. 62107035]. The authors would like to thank Weitao You and Zejian Li from Zhejiang University for their helpful comments. Besides, the authors would like to thank Liu Yanzhen and Lou Shanghua for their work on the literature review and the analysis process.
Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.
PY - 2022/4
Y1 - 2022/4
N2 - With the increasing prevalence of artificial intelligence (AI) agents, the transparency of agents has become vital in addressing interaction issues (e.g., trust, usefulness, and understandability). However, determining the transparency of AI agents requires a systematic consideration of complex related factors, including stakeholders, algorithms, context, etc. Thus, in our study, we presented an overview of studies on the transparency of AI agents through multiple-stage bibliometric analysis, and identified an ontological framework of the key concepts relevant to transparent AI. We then built a Transparent-AI Blueprint prototype which is a diagram that visualizes the ontological framework of design concepts. In the subsequent pilot test, we updated Blueprint to the final version, and validated it in a workshop. Our work structurally summarized the design concepts related to the transparency of AI agents, and proposed a useful and practical conceptual design tool that effectively guides designers to operationalize the transparency of AI agents.
AB - With the increasing prevalence of artificial intelligence (AI) agents, the transparency of agents has become vital in addressing interaction issues (e.g., trust, usefulness, and understandability). However, determining the transparency of AI agents requires a systematic consideration of complex related factors, including stakeholders, algorithms, context, etc. Thus, in our study, we presented an overview of studies on the transparency of AI agents through multiple-stage bibliometric analysis, and identified an ontological framework of the key concepts relevant to transparent AI. We then built a Transparent-AI Blueprint prototype which is a diagram that visualizes the ontological framework of design concepts. In the subsequent pilot test, we updated Blueprint to the final version, and validated it in a workshop. Our work structurally summarized the design concepts related to the transparency of AI agents, and proposed a useful and practical conceptual design tool that effectively guides designers to operationalize the transparency of AI agents.
UR - http://www.scopus.com/inward/record.url?scp=85134149651&partnerID=8YFLogxK
U2 - 10.1080/10447318.2022.2093773
DO - 10.1080/10447318.2022.2093773
M3 - Journal article
AN - SCOPUS:85134149651
SN - 1044-7318
VL - 38
SP - 1846
EP - 1873
JO - International Journal of Human-Computer Interaction
JF - International Journal of Human-Computer Interaction
IS - 18-20
ER -