Artificial Intelligence for Evidence-Based Policy Design: Integrating Data, Simulation and Societal Impact Assessment

John T. Spargo

Artificial Intelligence for Evidence-Based Policy Design: Integrating Data, Simulation and Societal Impact Assessment

Author(s)	John T. Spargo
Country	United States
Abstract	Evidence-based policy design increasingly depends on the ability of governments and public institutions to integrate large volumes of heterogeneous data, evaluate alternative interventions and anticipate their social and economic consequences. Artificial intelligence (AI), machine learning, simulation and advanced analytics provide emerging capabilities for strengthening this process. This paper examines the role of AI in evidence-based policy design, focusing on data integration, predictive modelling, policy simulation, causal analysis, impact assessment and continuous policy evaluation. A conceptual framework is proposed in which administrative data, socioeconomic indicators, survey information and real-world evidence are combined with AI models and simulation environments to evaluate alternative policy scenarios before implementation. Particular attention is given to the distinction between prediction and causation, as accurate forecasting alone does not establish that a proposed policy will produce a desired outcome. The paper also examines challenges involving data quality, algorithmic bias, privacy, representativeness, model explainability, institutional capacity and democratic accountability. A human-centred policy intelligence framework is proposed in which AI functions as an analytical and decision-support capability while elected representatives, policymakers, domain experts and affected communities retain responsibility for normative and political decisions. The analysis suggests that AI can strengthen evidence-based policymaking by enabling faster synthesis of evidence, scenario comparison and continuous impact monitoring, but its effectiveness depends on transparent methodologies, high-quality data, robust causal reasoning and meaningful societal participation. Responsible integration of AI could transform policy development from a predominantly periodic process into a more adaptive, data-informed and continuously evaluated system.
Keywords	Artificial Intelligence, Evidence-Based Policy, Policy Design, Machine Learning, Policy Simulation, Impact Assessment, Public Policy, Causal Inference, Data Analytics, Social Impact.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-02

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Artificial Intelligence for Evidence-Based Policy Design: Integrating Data, Simulation and Societal Impact Assessment

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