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Trust in artificial intelligence within production management - an exploration of antecedents
[journal article]
Abstract Industry 4.0, big data, predictive analytics, and robotics are leading to a paradigm shift on the shop floor of industrial production. However, complex, cognitive tasks are also subject of change, due to the development of artificial intelligence (AI). Smart assistants are finding their way into the... view more
Industry 4.0, big data, predictive analytics, and robotics are leading to a paradigm shift on the shop floor of industrial production. However, complex, cognitive tasks are also subject of change, due to the development of artificial intelligence (AI). Smart assistants are finding their way into the world of knowledge work and require cooperation with humans. Here, trust is an essential factor that determines the success of human-AI cooperation. Within this article, an analysis within production management identifies possible antecedent variables on trust in AI and evaluates these due to interaction scenarios with AI. The results of this research are five antecedents for human trust in AI within production management. From these results, preliminary design guidelines are derived for a socially sustainable human-AI interaction in future production management systems. Practitioner summary: In the future, artificial intelligence will assist cognitive tasks in production management. In order to make good decisions, humans trust in AI has to be well calibrated. For trustful human-AI interactions, it is beneficial that humans subjectively perceive AI as capable and comprehensible and that they themselves are digitally competent.... view less
Keywords
digitalization; engineering; effects of technology; technology; data; artificial intelligence; production; knowledge work; economic cooperation
Classification
Technology Assessment
Sociology of Science, Sociology of Technology, Research on Science and Technology
Free Keywords
Production management; cyber production management; trustartificial intelligence; machine learning
Document language
English
Publication Year
2021
Page/Pages
p. 1333-1350
Journal
Ergonomics, 64 (2021) 10
DOI
https://doi.org/10.1080/00140139.2021.1909755
ISSN
0014-0139
Status
Published Version; peer reviewed
Licence
Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0