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

Qinshu Yu ()
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Qinshu Yu: Nanyang Technological University

A chapter in Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), 2026, pp 146-157 from Springer

Abstract: Abstract 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.

Keywords: decision systems; algorithmic governance; judgment modeling; risk compression mechanisms; rollback-capable system design (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-689-0_14

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