TY - GEN
T1 - Capturing the Trends, Applications, Issues, and Potential Strategies of Designing Transparent AI Agents
AU - Sun, Lingyun
AU - Li, Zhuoshu
AU - Zhang, Yuyang
AU - Liu, Yanzhen
AU - Lou, Shanghua
AU - Zhou, Zhibin
N1 - Funding Information:
This paper is funded by National Science and Technology Innovation 2030 Major Project (2018AAA0100703) of the Ministry of Science and Technology of China and the Provincial Key Research and Development Plan of Zhejiang Province (No. 2019C03137).
Publisher Copyright:
© 2021 ACM.
PY - 2021/5/8
Y1 - 2021/5/8
N2 - With the increasing prevalence of Artificial Intelligence (AI) agents, the transparency of agents becomes vital in addressing the interaction issues (e.g., explainability and trust). The existing body of research provides valuable theoretical and practical studies in this field. However, determining the transparency of AI agents requires the systematic consideration of the application categories and automation level, which is hardly considered by the prior literature. We thus apply the bibliometric analysis to gain insights from the published literature. Our work outlines the trend of how the number of studies about AI agent transparency increased over the years. We also identify the major application topics and issues in designing transparent AI agents. Furthermore, we categorize the identified applications according to the specific dimensions (risk and timeliness) and put forward potential strategies for designing different agents. Besides, we suggest the possible transparency degree corresponding to the automation level.
AB - With the increasing prevalence of Artificial Intelligence (AI) agents, the transparency of agents becomes vital in addressing the interaction issues (e.g., explainability and trust). The existing body of research provides valuable theoretical and practical studies in this field. However, determining the transparency of AI agents requires the systematic consideration of the application categories and automation level, which is hardly considered by the prior literature. We thus apply the bibliometric analysis to gain insights from the published literature. Our work outlines the trend of how the number of studies about AI agent transparency increased over the years. We also identify the major application topics and issues in designing transparent AI agents. Furthermore, we categorize the identified applications according to the specific dimensions (risk and timeliness) and put forward potential strategies for designing different agents. Besides, we suggest the possible transparency degree corresponding to the automation level.
KW - AI
KW - Transparency
KW - Transparent agent
KW - Trend
UR - http://www.scopus.com/inward/record.url?scp=85105786719&partnerID=8YFLogxK
U2 - 10.1145/3411763.3451819
DO - 10.1145/3411763.3451819
M3 - Conference article published in proceeding or book
AN - SCOPUS:85105786719
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI EA 2021
PB - Association for Computing Machinery
T2 - 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021
Y2 - 8 May 2021 through 13 May 2021
ER -