Artificial Intelligence for data-driven decision-making and governance in public affairs |
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| Institution: | 1. School of Management, University of Bradford, Bradford, UK;2. College of Business and Economics, Qatar University, Doha, Qatar;3. The University of New South Wales, Sydney, Australia;1. FSA ULaval, Université Laval, Pavillon Palasis-Prince, 2325 rue de la Terrasse, Quebec, QC G1V 0A6, Canada;1. Department of Organization, University of Zagreb, Faculty of Organization and Informatics Vara?din, Pavlinska 2, 42 000 Vara?din, Croatia;2. College of Arts and Sciences, Carlow University, 3333 Fifth Avenue, Pittsburgh, PA 15213, United States;1. Centre for Digital Business Research, Westminster Business School, University of Westminster, 35 Marylebone Road, London NW1 5LS, United Kingdom;1. Party School of the Chengdu Committee of the Chinese Communist Party, Chengdu 610110, China;2. School of Management, Fudan University, Shanghai 200433, China;3. Business School, University of Nottingham Ningbo, Ningbo 315100, China;1. College of Humanities and Social Development, Northwest A&F University, Yangling 712100, China;2. School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, China;3. School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China;1. Associate Professor and Director, School of Planning, Public Policy, and Management, University of Oregon, 263 Hendricks Hall, 1209 University of Oregon, Eugene, OR 97403, United States of America;2. Associate Professor, School of Urban Affairs, Cleveland State University, Cleveland, OH 44115, United States of America |
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| Abstract: | Literature shows there is a growing interest in studies involving Artificial Intelligence (AI) in the public sector; and while there is evidence of many governmental initiatives that have been established to harness the power of AI, empirical research on the topic and evidence-based insights are rather lacking. The aim of this Special Issue on Artificial Intelligence for Data-Driven Decision-Making and Governance in Public Affairs is to extend both the theoretical and practical boundaries of AI research in the public sector in order to improve governmental decision-making and governance, thus enhancing public value creation. The papers in this special issue focus on AI risks and guidelines, AI governance, the risks of governmental implementation of AI to citizens' privacy, increasing citizen satisfaction through AI-enabled government services, the enablers and challenges of AI implementation in specific public sectors, and using AI to study political opinion. These papers not only advance our knowledge and understanding of the use of AI in government and public governance, but they also help to set out a renewed research agenda. Future research should, among other things, focus on inter- and multi-disciplinary empirical studies that call for the collaboration of a variety of stakeholders; on the longitudinal dynamics of creating public value through the breadth and depth of AI assimilation; and on the investigation of the ethical challenges (particularly data privacy) in AI implementation. |
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