Designing a Public Policy-Making Model Based on Artificial Intelligence

Document Type : Research Paper

Authors

1 PhD student of public administration, majoring in decision-making and public policy, University of Tehran.Iran.

2 Full Professor, College of Management and Faculty of Public Administration and Organization Science, Department of Public Policy and Administration, University of Tehran, Iran.

3 Full Professor,College of Management and Faculty of Public Administration and Organization Science, University of Tehran, Iran.

10.22111/jmr.2025.48083.6156

Abstract

Abstract
In the past, public policy-making systems were involved in all stages of policy-making, from problem identification to termination and documentation. However, they were unable to precisely compare the costs and benefits of options. During thisperiod, scholars tried to develop the literature of policy-making and policy research to provide potential capacities for the success of public policies and to offer a kind of strategic insight in dealing with public issues to policy-makers (regardless of evaluating all possible alternatives). This was done with the hope that through trial and error, and in many cases, regardless of the sequential consequences of material, social, and even intergenerational costs, public issues could be solved within the framework of satisfactory models.
However, today, due to the unprecedented capacity of knowledge derived from artificial intelligence and data-driven decision-making intelligence, the realization of a rational policy-making model seems more feasible.
Introduction
Historically, public policymaking systems operated within inherent limitations—constrained by informational deficits and analytical incapacity—leading to an acceptance of systemic inefficacy. The nascent discipline of public policy thus emerged amid foundational shortcomings. However, within a century of conceptualizing policy autonomy, a resurgence of rationality is now imperative. Modern societies, armed with AI-driven decision intelligence and computational prowess (Xu et al., 2021), are redefining governance through machine learning, predictive algorithms, and data-centric paradigms (Eyert et al., 2022). Innovations like autonomous transport and algorithmic regulation (Jung, 2022) exemplify AI’s transformative potential in public administration. Healthcare analytics (Morley et al., 2022) and equitable resource allocation (Robles & Mallison, 2023) demonstrate AI’s capacity to enhance efficacy and equity. This evolution prompts a critical inquiry: does AI herald a new paradigm (Hood, 1991) in policymaking? By integrating stakeholder interests (Gellers, 2021) and leveraging big data, AI promises to mitigate inequalities and optimize resource distribution. The exponential growth of AI research (Duan et al., 2019) underscores its role in advancing decision-support systems, expert systems, and scenario-based policymaking, ultimately redefining rationality in governance.
Case study
Interviews were conducted with 11 expert specialists in the fields of public administration, public­­­­­­policy-making, artificial intelligence, programming, and philosophy of language
Materials and Methods
This research employs a qualitative approach utilizing the classic grounded theory method (Glaser, 1978:7). In terms of purpose, it is an applied study designed to advance public policy knowledge through the application of artificial intelligence and rational decision-making frameworks.
Discussion and Results
The AI-based public policy-making model presents a transformative approach that comprehensively addresses current and future societal needs through advanced spatiotemporal analytical frameworks. This innovative model integrates policy science with artificial intelligence capabilities to systematically rationalize the construction of social reality, creating a dynamic innovation ecosystem where interconnected cognitive policy networks process vast amounts of public data to inform political and administrative decision-making processes. The model's architecture demonstrates several distinctive features: its data governance framework facilitates real-time information aggregation, continuous machine learning, and evidence-based decision cycles while rigorously maintaining ethical governance protocols.
Conclusion
 The system generates balanced policy outputs through sophisticated algorithmic analysis of alternatives, effectively filtering out impractical options and promoting intergenerational equity. Its innovation infrastructure includes policy laboratories, intelligent decision-support mechanisms, and comprehensive performance assessment modules that leverage AI's predictive analytics capabilities. However, the implementation landscape faces significant challenges, including the risk of policy system failure due to the accumulation of unenforceable measures that may exacerbate bureaucratic inefficiencies and social disorder. Furthermore, limitations in human and material capital investment threaten the ecosystem's sustainability and may intensify brain drain and the proliferation of unsubstantiated knowledge systems. This fundamental paradigm shift redefines traditional notions of policy rationality through AI's unparalleled analytical capacities while simultaneously addressing the complex challenges of systemic vulnerability in modern governance structures. The convergence of computational policy-making and institutional frameworks heralds a new era of evidence-based governance, though its successful implementation requires careful navigation of both technological and socio-political dimensions.

Keywords

References

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