DFAS-EconomyGov: A Conceptual Governance Doctrine for Algorithmic Market Integrity and Supply Chain Resilience
DOI:
https://doi.org/10.31098/ijmesh.v10i1.4666Keywords:
AI Governance, Algorithmic Market Integrity, Supply-Chain Resilience, Economic Sovereignty, DFAS-Economygov.Abstract
Artificial intelligence (AI) is increasingly embedded in financial markets, trade, and supply-chain decision systems, creating opportunities for efficiency and resilience while introducing governance problems involving opacity, algorithmic interaction, technological dependence, supply-chain fragility, and jurisdictional misalignment. Existing governance approaches address important elements of AI ethics, risk management, accountability, regulation, and resilience, but these functions remain only partially integrated within economically consequential AI-mediated decisions. This study employs a non-empirical doctrinal and conceptual design approach. Evidence from peer-reviewed research, regulatory and institutional publications, and international standards is synthesized to identify recurring governance problems and derive corresponding requirements. These requirements are translated into a governance doctrine and evaluated through evidence-based comparative analysis and three illustrative conceptual scenarios, including a failure-oriented stress scenario. The scenarios demonstrate conceptual operation and boundaries rather than empirical validation. The study develops DFAS-EconomyGov, centered on the Alaali Economy Governance Doctrine (A-EGD) and operationalized through four supporting functions: explainability escalation (A-MEEF), authority-bounded intervention (A-TOCC), jurisdiction-sensitive calibration (A-SCETM), and optional governance traceability (A-MIL). Comparative analysis indicates that established instruments provide substantial coverage of individual governance functions, while DFAS-EconomyGov differs primarily in their configuration. Scenario analysis identifies applicability boundaries arising from poorly calibrated intervention, conflicting jurisdictional requirements, and insufficient technical or contractual control over third-party AI systems. The contribution is configurational rather than elemental: DFAS-EconomyGov connects detection and explainability to escalation, intervention authority, jurisdiction-sensitive calibration, and traceability within a single economic governance doctrine. The study establishes conceptual structure, comparative positioning, and identifiable failure conditions, but not empirical effectiveness or superiority.
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Copyright (c) 2026 Hasan Mohamed Husain Alaali
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