Affiliations: [a] Department of Political Science, National Taiwan University, Taipei, Taiwan | [b] School of Public Administration, University of Nebraska at Omaha, Omaha, USA | [c] Department of Management Information Systems, National Chengchi University, Taipei, Taiwan | [d] School of Computing and Information, University of Pittsburg, Pittsburgh, USA
Correspondence:
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Corresponding author: Yi-Fan Wang, Department of Political Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan. E-mail: [email protected].
Abstract: Artificial intelligence (AI) applications have been emerging in these past years and affecting multiple dimensions of the public sector. The government utilizes AI to transform policy implementation and service delivery, but AI can also threaten citizens’ privacy and social equity due to its potential biases. These concerns increase citizens’ perceived uncertainty concerning AI. In an uncertain environment, trust transfer serves as a way to improve citizens’ trust in AI-enabled government systems. However, little research has explored trust transfer between the public sector and the system. This study examines whether a context-based trust transfer mechanism can explain the trust-building of the AI-enabled government system. The study conducted a survey and analyzed the collected data using factor-score-based regression analysis. The research results indicate that trust transfer occurs for the AI-enabled government system. Trust in an administrative process, local government, and political leaders can be transferred to trust in governmental AI systems. The findings can advance the theoretical development of trust transfer theory and be used to develop recommendations for the public sector.
