EXPLAINABLE AI-DRIVEN DECISION INTELLIGENCE FRAMEWORK FOR UNCERTAIN AND HIGH-STAKES ENVIRONMENTS IN SOCIOECONOMIC AND POLICY DOMAINS WITH MULTI-CRITERIA TRUST-AWARE SYSTEM INTEGRATING CAUSAL INFERENCE, GAME THEORY, AND HUMAN-IN-THE-LOOP OPTIMIZATION

The increasing reliance on AI systems for decision-making in high-stakes environments—such as public policy, national security, and socioeconomic forecasting—demands frameworks that are not only intelligent but also explainable and trustworthy. This paper proposes a novel decision intelligence framework that leverages Explainable Artificial Intelligence (XAI), causal inference, game theory, and human-in-the-loop optimization. We aim to address the critical challenge of operational transparency and multi-criteria trust in AI-powered policy systems. Our contextual framework harmonizes predictive accuracy, stakeholder interpretability, and social robustness, presenting a pathway toward democratized, accountable, and resilient policy AI ecosystems.