Judgment Cannot be Outsourced: Modeling Non-Computable Decision Boundaries in Large-Scale Algorithmic Systems

In large-scale digital institutions, formalized processes, compliance mechanisms, and algorithmic systems are essential for managing complexity and operational scale. However, these structures increasingly treat judgment as a computable and outsourcable function. This paper argues that such reduction introduces a structural failure mode: systems may appear operationally stable while progressively losing their capacity for fact recognition, correction, and reality responsiveness. Judgment is reframed not as a moral attribute but as a non-compressible system-level decision interface composed of situational awareness, action convergence under uncertainty, and responsibility anchoring. From a systems-engineering perspective, compliance mechanisms, KPI-driven incentives, and algorithmic delegation operate as risk-compression structures. When granted veto authority over factual signals, these mechanisms drive a phase transition from rollback-capable failure to irreversible systemic drift. We formalize this distinction by separating computable decision layers from non-computable judgment boundaries in socio-technical systems. The analysis explains how decision opacity and delayed error amplification emerge when responsibility anchors are weakened. Design implications are provided for algorithmic governance and human-in-the-loop architectures, emphasizing the preservation of interruptible and accountable judgment interfaces.