Decision Infrastructure: Redefining AI Systems Through Governance, Irreversibility, and Human Authority

This paper introduces the concept of Decision Infrastructure as a missing computational layer in modern artificial intelligence systems. While current AI architectures focus primarily on prediction and optimization, they lack formal mechanisms for governed decision-making, irreversibility management, and authority-constrained execution. The proposed framework defines decision-making as a structured, mathematical process integrating probability, impact, irreversibility, and uncertainty into a unified risk-based model. A governance-first architecture is introduced, where decisions are filtered through constraint systems and executed only under human-final authority. The model extends traditional risk formulations by incorporating irreversibility as a non-linear amplifier and uncertainty as a time-dependent function. This enables dynamic, risk-aware decision evaluation in complex environments. Through simulation and cross-domain case studies in financial systems and energy infrastructure, the framework demonstrates its ability to reduce systemic risk, prevent uncontrolled automation, and improve decision accountability. Decision Infrastructure represents a paradigm shift from optimization-driven AI systems to governed decision systems, establishing a foundation for future computational architectures in high-stakes environments. Keywords: Decision Infrastructure, Governance-First AI, Risk Modeling, Irreversibility, Human-in-the-Loop, AI Governance, Financial Systems, Energy Systems, Decision Theory