TY - JOUR
T1 - Exploring the Complexity of AI Applications in the Public Sector
T2 - 2024 Ongoing Research, Practitioners, Posters, Workshops, and Projects of the International Conference EGOV-CeDEM-ePart, EGOV-CeDEM-ePart-Ongoing 2024
AU - Huang, Zong Xian
AU - Gil-Garcia, J. Ramon
AU - Gascó-Hernández, Mila
N1 - Publisher Copyright:
© 2023 Copyright for this paper by its authors.
PY - 2024
Y1 - 2024
N2 - Artificial intelligence (AI) has been deployed in many government contexts and with very different results in countries around the world. There seems to be a distinct transformational power when compared with previous technologies. However, it is not clear how different characteristics of AI systems affect their purpose and outputs. Therefore, by understanding some of the unique characteristics of self-learning systems in the context of a proposed typology consisting of two dimensions-visibility and autonomy-this study explores the interplay of visibility, autonomy, and self-learning in government AI systems. Based on the analysis of four distinct AI cases across diverse U.S. federal agencies, this ongoing research paper aims to uncover some of the opportunities and challenges posed by AI and specifically self-learning as one of its main features. Our preliminary results underscore the necessity of contextual analysis in deploying AI systems, thereby contributing to previous research on different characteristics and types of AI.
AB - Artificial intelligence (AI) has been deployed in many government contexts and with very different results in countries around the world. There seems to be a distinct transformational power when compared with previous technologies. However, it is not clear how different characteristics of AI systems affect their purpose and outputs. Therefore, by understanding some of the unique characteristics of self-learning systems in the context of a proposed typology consisting of two dimensions-visibility and autonomy-this study explores the interplay of visibility, autonomy, and self-learning in government AI systems. Based on the analysis of four distinct AI cases across diverse U.S. federal agencies, this ongoing research paper aims to uncover some of the opportunities and challenges posed by AI and specifically self-learning as one of its main features. Our preliminary results underscore the necessity of contextual analysis in deploying AI systems, thereby contributing to previous research on different characteristics and types of AI.
KW - AI autonomy
KW - AI typology
KW - AI visibility
KW - self-improvement
KW - self-learning
UR - http://www.scopus.com/inward/record.url?scp=85200745964&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85200745964
SN - 1613-0073
VL - 3737
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 1 September 2024 through 5 September 2024
ER -