不可逆性作为 AI 增强性人类判断系统设计的第一性原理 (Irreversibility as a First-Order Design Principle in AI-Augmented Human Judgment Systems)

Abstract In contemporary Human–AI systems, failures increasingly arise not from biased judgments, but from the irreversible loss of human judgment authority itself. Existing AI governance and decision-support frameworks remain grounded in reversible, optimization-centric assumptions, rendering them structurally incapable of addressing irreversible cognitive, organizational, and capability degradation induced by AI-mediated decision loops. This paper establishes irreversibility as a first-order design and governance principle for AI-augmented human judgment systems by formally introducing the concept of Irreversibility–Judgment Human–Augmented Systems (IJ-HJAS). Building upon the philosophical foundations of Capability System Science (CSS) and the governance logic of Irreversibility-First Risk Governance (IFRG), irreversibility is reframed not as an exceptional risk category, but as a governing axis shaping judgment loops, capability formation, and survivability under non-equilibrium conditions. The paper traces the genealogical origin of irreversibility from non-equilibrium thermodynamics to its formalization within CESI and CSS, articulates its operationalization through IFRG, and establishes its decisive role in AI-era judgment system design. IJ-HJAS further formalizes the principles of irreversible judgment gating, narrative hijacking detection, and AI as epistemic amplifier, ensuring human judgment loops remain protected under high-stakes, irreversible conditions. By repositioning irreversibility as a cross-layer primitive spanning cognition, organization, and AI system architecture, this work provides a foundational framework for designing Human–AI systems that remain judgment-capable under irreversible pressure.