This working paper introduces Judgment Infrastructure as a system-level framework for analyzing AI governance beyond individual models, ethical checklists, or isolated decision systems. Rather than treating education, administration, justice, and AI systems as separate governance silos, the paper conceptualizes them as interdependent domains within a System City, where judgments travel, accumulate, transform, and shed responsibility across institutional boundaries. Many contemporary failures in AI-assisted decision-making, the paper argues, arise not from any single system, but from the infrastructural flow of judgment itself. The framework focuses on how classifications, routings, and evidentiary weight migrate across public systems, giving rise to recurring structural risks such as judgment surrender, decision drift, and responsibility fallback. These risks are shown to be systemic properties of interconnected socio-technical environments, particularly under conditions of scale, automation, and institutional fragmentation. The paper further introduces the concept of contestation windows as a governance design principle—points within judgment flows where inherited classifications can be meaningfully reviewed or overridden by human decision-makers, allowing responsibility to re-enter decision processes before harm becomes irreversible. This document represents a Layer-3 conceptual contribution within a broader research program on system-level judgment and responsibility flow. It deliberately excludes lower-layer representational systems, state definitions, thresholds, or generative rules. Visual elements included in the paper depict relational dynamics only and do not encode underlying state architectures. The working paper is intended for researchers, policymakers, and practitioners engaged in AI governance, public decision systems, and socio-technical risk analysis. It is suitable for citation as a conceptual governance framework and as a foundation for future empirical, comparative, or policy-oriented work.
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Sincere Ann Ma
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Sincere Ann Ma (Tue,) studied this question.
synapsesocial.com/papers/6971be6b642b1836717e30c4 — DOI: https://doi.org/10.5281/zenodo.18312786