Artificial Intelligence and the Paradigm Shift in Public Policy: Between Innovation Opportunities and Emerging Risks

Abstract

This study aims to examine the paradigm shift brought about by artificial intelligence (AI) in public policy scholarship. It explores how machine learning, big data analytics, and generative AI reshape the core logic of policymaking across the policy cycle. The paper highlights the contribution of these technologies to enhancing predictive capabilities, optimizing data analytics, supporting decision-making, and streamlining policy implementation and evaluation. Consequently, it marks a transition from the conventional linear framework to a dynamic, proactive model driven by data and algorithms. The study further critically addresses the risks inherent in this transformation, particularly algorithmic bias, the lack of transparency, the "black box" problem, privacy concerns, and diminished accountability. Methodologically, the research employs a descriptive-analytical approach coupled with a systems framework. It concludes that while AI serves as a fundamental driver of this paradigm shift in public policy, its success remains contingent upon establishing a robust legislative, ethical, and institutional framework that strikes a balance between technical efficiency and human oversight, thereby advancing the public interest and safeguarding policy legitimacy in the digital era.