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.

This study aims to advance the theory and practice of managing collaborative data networks for information and decision-support services that exist in over 400 US metropolitan areas. Integrating insights from collaborative governance, network management, and cross-boundary information sharing, this study develops a framework to outline the interplay between context, management, collaborative dynamics, technology, and performance. This study further utilizes the framework to conduct an exploratory in-depth case study of a metropolitan transportation data network to examine such interplay. The findings suggest ways to improve the performance of collaborative data networks and their implications are discussed.

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.

This study aims to advance our understanding of online transparency for open government by focusing on the conditions affecting the behavior of public agencies. By drawing from literature on open government, collaborative governance, and bureaucratic behavior, the study identifies key conditions for transparency. The empirical investigation builds on citizen-initiated policy proposals on an online collaborative platform and utilizes fuzzy set qualitative comparative analysis. The results support a risk-management perspective for agency-level transparency behavior. There are multiple configurations for the provision of policy and collaboration information. The lack of policy conflict is a necessary condition for information provision.