Management Science and Information Technology

Management Science and Information Technology

AI-Enabled Policy Intelligence for Social Resilience: A Meta-Synthesis Framework Across Prediction, Evaluation, and Implementation

Document Type : SEMIT 2025

Authors
Mohammad Mehdi Namdar Joybari 1
Saeid Rezaei 2
1 Department of Policy and Public Affairs, University of Tehran, Tehran, Iran
2 Department of Industrial and Systems Engineering, University of Arak, Arak, Iran
10.22034/ISS.2026.10275.1050
Abstract
Objective: Governments increasingly deploy artificial intelligence (AI) to enhance policy responsiveness; however, resilience gains remain uneven because of persistent gaps in data readiness, evaluation capacity, and accountable implementation. This study develops an AI-enabled policy intelligence framework that integrates AI capabilities across the policy cycle—prediction, evaluation, and implementation—to strengthen social resilience under accountable governance constraints.
Methods: A qualitative meta-synthesis was conducted on 20 peer-reviewed studies, one international report, and five Iranian national policy documents published between 2010 and 2024. Using structured coding and thematic synthesis procedures, evidence was consolidated into six operational dimensions: big-data sensing, predictive modeling, real-time monitoring, fairness assessment, decision optimization, and smart public service delivery. Reliability was assessed through inter-coder agreement on a double-coded subset using Cohen’s κ (κ = 0.756), complemented by expert-panel validation using the Content Validity Ratio (CVR).
Results: Findings show that AI can reduce decision latency, improve policy responsiveness, and expand evaluative coverage across the policy cycle. Nevertheless, resilience outcomes depend heavily on governance safeguards that address algorithmic bias, privacy protection, explainability, automation bias, and cybersecurity risks. The analysis further demonstrates that AI effectiveness in public governance is contingent on institutional oversight and accountable implementation mechanisms.
Conclusion: The proposed framework provides a cycle-spanning roadmap for policymakers seeking to operationalize AI for resilience-oriented governance. It highlights the strategic importance of data governance, human oversight, and risk-control mechanisms in maximizing AI-enabled resilience benefits, particularly in crisis contexts such as pandemics and other large-scale societal disruptions.
Keywords
AI-enabled policymaking
policy intelligence
social resilience
algorithmic governance
policy evaluation analytics
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Management Science and Information Technology
Volume 3, Issue 1
Winter 2026
Pages 49-71