Artificial Intelligence (AI) in Public Policy Reform: Approaches, Challenges, And Outcomes
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Abstract
Artificial Intelligence (AI) plays a crucial role in driving public policy reform by enhancing bureaucratic efficiency, improving the quality of decision-making, and enabling the personalization of public services. Technologies such as machine learning, predictive analytics, and automated decision-making systems enable governments to process large-scale data, anticipate societal needs, and optimize service delivery across various sectors. This study employs a Systematic Literature Review (SLR) by analyzing 40 peer-reviewed articles published between 2010 and 2024 to examine approaches to AI implementation, associated challenges, outcomes, and adoption patterns in public policy. The findings indicate that AI has been widely adopted in sectors such as healthcare, security, and public administration. The implementation of these technologies has demonstrably improved operational efficiency, enhanced decision-making quality, and supported more adaptive and personalized public services. Nevertheless, the application of AI in the public sector continues to face significant challenges, including algorithmic bias, ethical concerns, technical limitations, regulatory constraints, and data privacy risks. Cross-sectoral analysis also reveals disparities in adoption levels, with developed countries exhibiting higher degrees of AI integration than developing countries. Overall, this study highlights the substantial potential of AI in advancing data-driven governance. Therefore, ethical AI governance, transparent regulatory frameworks, and cross-sector collaboration are essential to ensure the responsible and sustainable implementation of AI.
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