Deterministic Governance of High-Risk AI Decisions Under Irreversibility Constraints

This paper introduces a deterministic governance framework for high-risk artificial intelligence decision systems operating under conditions of irreversibility and uncertainty. While existing AI approaches primarily focus on predictive accuracy and post-hoc explainability, they lack formal mechanisms to constrain decision execution in environments where outcomes may be non-recoverable. The proposed model reframes decision-making as a constrained optimization problem, integrating probability, impact, irreversibility, and uncertainty into a unified risk function. A governance threshold is introduced to enforce admissibility conditions prior to execution, transforming AI systems from prediction-driven architectures into controlled decision systems. The framework is model-agnostic and can be applied across critical domains such as energy infrastructure, financial systems, and large-scale logistics. By separating predictive intelligence from execution authority, the approach enhances reliability, auditability, and systemic safety. This work contributes a mathematically grounded and operationally applicable solution to one of the most pressing challenges in AI governance: how to prevent the execution of high-risk decisions before irreversible consequences occur. The results demonstrate that constraint-based governance provides a scalable and robust foundation for deploying AI in high-impact environments.