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

DOI

10.2991/978-94-6239-689-0_14How to use a DOI?

Keywords

decision systems; algorithmic governance; judgment modeling; risk compression mechanisms; rollback-capable system design

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qinshu Yu
PY  - 2026
DA  - 2026/05/28
TI  - Judgment Cannot be Outsourced: Modeling Non-Computable Decision Boundaries in Large-Scale Algorithmic Systems
BT  - Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026)
PB  - Atlantis Press
SP  - 146
EP  - 157
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-689-0_14
DO  - 10.2991/978-94-6239-689-0_14
ID  - Yu2026
ER  -