Artificial Intelligence-Based Decision Support Systems in Public Administration

Leslie Pack Kaelbling

Artificial Intelligence-Based Decision Support Systems in Public Administration

Author(s)	Leslie Pack Kaelbling
Country	United States
Abstract	Artificial Intelligence (AI) has emerged as a transformative technology in public administration by enhancing decision-making, service delivery, policy formulation, and governance through intelligent data analysis and predictive analytics. Traditional decision-making processes in public institutions often rely on manual data processing, fragmented information systems, and reactive administrative approaches, limiting the efficiency, transparency, and responsiveness of government operations. Artificial Intelligence-Based Decision Support Systems (AI-DSS) integrate Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Natural Language Processing (NLP), cloud computing, edge computing, Geographic Information Systems (GIS), and the Internet of Things (IoT) to enable evidence-based policymaking, real-time public service management, and intelligent governance. These systems assist government agencies in resource allocation, disaster management, healthcare administration, public safety, taxation, urban planning, environmental management, and citizen service delivery. This study investigates AI-based decision support systems in public administration using a qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, government reports, international policy documents, and smart governance case studies. The study explores the architecture of AI-driven decision support systems, intelligent governance frameworks, predictive public policy analysis, citizen-centric digital services, and smart government initiatives. The findings indicate that AI-powered decision support systems significantly improve administrative efficiency, policy accuracy, public service quality, transparency, accountability, and strategic resource management. Machine Learning algorithms analyse structured and unstructured public data to predict policy outcomes, identify service delivery gaps, optimise resource allocation, detect fraud, and support evidence-based decision-making. Integration with Digital Twins, blockchain, cloud platforms, and explainable AI further strengthens intelligent governance, public trust, and regulatory compliance. However, challenges including data privacy, cybersecurity, algorithmic bias, ethical concerns, interoperability, workforce readiness, and regulatory governance remain significant barriers to large-scale implementation. The study concludes that AI-based decision support systems are essential for modernising public administration and achieving transparent, efficient, inclusive, and sustainable governance in the digital era.
Keywords	Artificial Intelligence, Decision Support Systems, Public Administration, Smart Governance, Machine Learning, Digital Government, Big Data Analytics, Explainable AI, Public Policy, E-Government.
Field	Engineering
Published In	Volume 3, Issue 1, January-February 2021
Published On	2021-01-24

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Artificial Intelligence-Based Decision Support Systems in Public Administration

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