While there has been growth in the literature exploring the governance of artificial intelligence (AI) and recognition of the critical importance of guiding public values, the literature lacks a systematic study focusing on public values as well as the governance challenges and solutions to advance these values. This article conducts a systematic literature review of the relationships between the public sector AI and public values to identify the impacts on public values and the governance challenges and solutions. It further explores the perspectives of U.S. government employees on AI governance and public values via a national survey. The results suggest the need for a broad inclusion of diverse public values, the salience of transparency regarding several governance challenges, and the importance of stakeholder participation and collaboration as governance solutions. This article also explores and reports the nuances in these results and their practical implications.

Artificial intelligence (AI) applications in public services are an emerging and crucial issue in the modern world. Many countries utilize AI-enabled systems to serve citizens and deliver public services. Although AI can bring more efficiency and responsiveness, this technology raises privacy and social inequality concerns. From the perspective of behavioral public administration (BPA), citizens’ use of AI-enabled systems depends on their perception of this technology. This study proposes a conceptual framework connecting citizens’ perceptions, trust, and intention to follow instructions from the government-supported AI-enabled recommendation system in the pandemic. Our study launches an online-based experimental survey and analyzes the data with the partial least square structural equation model (PLS-SEM). The research findings suggest that algorithmic transparency increases trust in the recommendations, but privacy concerns decrease the trust when the system asks for sensitive information. Additionally, citizens familiar with technologies are more likely to trust the recommendations in the feature-based communication strategy. Finally, trust in the recommendations can mediate the impacts of citizens’ perceptions of the AI system. This study clarifies the effects of perceptions, identifies the role of trust, and explores the communication strategies in citizens’ intention to follow the AI-enabled system recommendations. The results can deepen AI research in public administration and provide policy suggestions for the public sector to develop strategies to increase policy compliance with system recommendations.

In this article, we explore the challenges of global governance and the particular challenge presented by global data governance. We discuss a range of challenges to developing meaningful global governance institutions for regulating how companies and governments around the world manage and utilize consumer data. These challenges are compounded by their global nature and the complexities of Internet-based technologies. We argue that the following gaps exist for effective global data governance: (a) there is no overarching global framework for protecting consumer data, and it is partial and incomplete; (b) there is a lack of data protection for international data transfers, as much of the regulation that is being developed is not global in scale; and (c) new areas of data collection and use compound concerns to effective data governance in a globalized digital world. Moreover, we highlight important needs in terms of both global governance and impending challenges related to current and new uses of data. Any global governance framework should recognize the need for an iterative process where communication is ongoing between the necessary stakeholders. Agreements should incorporate common goals to maximize the potential development of global data governance norms. However, goals must remain flexible to the different data environments across nation-states while maintaining a global scope to ensure data protection. In addition, any agreement should consider the emerging challenges in this area. These challenges include new methods of data collection and use, as well as protecting individuals from manipulation and undue influence based on how their data are being used, processed, and collected.

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.