A Deterministic Decision Authority Framework for Governance-Grade AI Systems

BackgroundHigh-impact institutional decisions increasingly rely on AI-assisted analytics, yet most systems lack deterministic governance constraints. While explainability and audit logs provide post-hoc traceability, they do not formally condition decision authority under uncertainty, irreversibility, and time pressure. ObjectiveThis paper introduces a Deterministic Decision Authority Framework designed for governance-grade institutional environments. The architecture constrains decision activation through formalized irreversibility weighting, uncertainty decomposition, and time-bound authority gating. MethodologyThe proposed framework integrates: An irreversibility-weighted risk function Time-decay normalization of exposure Non-autonomous activation constraints Deterministic replay verification logic Human-final authority enforcement layer The system is mathematically structured to prevent unauthorized execution and to ensure reproducible audit trails. ArchitectureThe model separates intelligence generation from execution authority.Outputs are classified into governance states (e.g., GO / HOLD / NO-GO), with deterministic replay compatibility and uncertainty thresholds embedded at the decision gate level. ContributionUnlike autonomous AI models, this framework positions decision intelligence as constrained infrastructure rather than executable agency. The architecture formalizes authority boundaries, integrates irreversibility metrics, and enables institutional-grade compliance alignment. ImplicationsThe proposed structure supports high-impact domains such as finance, energy infrastructure, and public-sector governance, where decision errors carry asymmetric and irreversible consequences.