Institutional Decisions as First-Class Information Objects: An Implementation-Independent Conceptual Framework

This study proposes a conceptual framework for representing institutional decisions as implementation-independent information objects. Through a structured review of the literature on explainable artificial intelligence, AI governance, data provenance, decision ontologies, and information representation, it identifies a research gap concerning the absence of a unified representation of institutional decisions. The proposed framework models institutional decisions as structured information objects composed of evidence, context, policy, computational reasoning, governance constraints, and human judgment. Rather than introducing a new AI algorithm, the study establishes a theoretical foundation for future research on decision reliability, auditability, traceability, interoperability, and trustworthy institutional AI systems.