About this Research Topic

Background

The integration of Artificial Intelligence (AI) into administrative systems and processes represents a significant advancement in the public sector, promising to enhance efficiency, decision-making, and service delivery. Traditionally, public administration has faced persistent challenges such as resource allocation, policy implementation, and stakeholder engagement, often constrained by limited budgets and evolving societal needs. The emergence of AI technologies, including machine learning, natural language processing, and predictive analytics, offers innovative solutions to these challenges, optimizing operations and enabling more informed policy decisions. Recent studies have demonstrated AI's potential to streamline administrative tasks, improve public engagement, and enhance resource allocation. However, the integration of AI also raises critical concerns regarding ethics, transparency, and accountability, highlighting the need for comprehensive examination and robust governance frameworks. Despite these advancements, there remains a gap in understanding how to effectively and ethically integrate AI into public systems, ensuring that these technologies align with public values and complement human decision-making.

This Research Topic aims to explore the frontier of AI applications within administrative systems, focusing on both the opportunities and challenges they present. The primary objectives include examining the potential of AI to address administrative problems, such as automating document analysis, deploying AI-driven chatbots for public engagement, and utilizing predictive analytics for resource allocation. Additionally, the research seeks to address ethical considerations, governance frameworks, and the impact of AI on public sector employment and workforce development. By investigating these areas, the research aims to contribute to the development of AI solutions that are ethical, transparent, and aligned with public values.

To gather further insights into the integration of AI in administrative systems, we welcome articles addressing, but not limited to, the following themes:
• Case studies of successful AI integration in public administration
• Ethical considerations and governance frameworks for AI in public systems
• The impact of AI on public sector employment and workforce development
• Innovative AI technologies and their potential applications in public services
• Evaluation methodologies for assessing the impact of AI on administrative efficiency and public service delivery

This Research Topic invites contributions from academics, practitioners, and policymakers, aiming to catalyze a multidisciplinary dialogue on the use of AI to create more responsive, efficient, and equitable administrative systems.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Public Sector Management, Intellectual Property Protection, Data Privacy, Stakeholders, AI Algorithms

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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