Part 4 – Designing the Meaning Infrastructure: Governance as Interpretive Maintenance in AI-Mediated Institutions

Programme Context This preprint forms part of the research programme The Coherence Problem: How Institutions Learn, Drift, and Realign, which studies institutional decision systems as interpretive learning systems operating under conditions of complexity, scale, and delayed feedback. The programme integrates four complementary components:(1) architecture — the formal structure of decision-system learning,(2) mechanism — translation drift as a structural source of misalignment,(3) measurement — methods for observing translation coherence, and(4) design — governance as interpretive maintenance in AI-mediated environments. Together, the papers examine how organisations determine what matters, how meaning becomes encoded in governance artefacts, how translation drift arises as intent moves across governance layers, and how institutions can observe, maintain, and deliberately realign interpretive coherence over time. Description (Part 4) This preprint forms part of the research programme The Coherence Problem: How Institutions Learn, Drift, and Realign, which studies institutional decision systems as interpretive learning systems operating under conditions of complexity and delayed feedback. This paper examines the design implications of translation drift in environments increasingly mediated by digital artefacts and AI systems. It argues that contemporary governance infrastructures—templates, criteria frameworks, scoring models, dashboards, metrics, and algorithmic systems—function not only as decision supports but as meaning infrastructures that stabilise and propagate institutional interpretations over time. Building on prior work identifying translation drift as a structural mechanism through which interpretive coherence can decay across governance layers, the paper shows how digital mediation relocates drift into infrastructure. Evaluative assumptions may become embedded in criteria structures, model architectures, optimisation logics, and dashboards, where they can persist and propagate across decision cycles. Rather than proposing specific optimisation tools, the paper develops design principles for governance as interpretive maintenance—the ongoing work of preserving traceability, revisability, and alignment in decision systems. From this perspective, organisations require capabilities to inspect their own decision logic, exercise meta-governance over artefact ecosystems, and position AI systems as instruments of interpretive traceability rather than autonomous decision authorities. Relevant for organisational learning, governance design, AI governance, digital portfolio management, and institutional decision systems operating under conditions of scale, complexity, and delayed feedback. Version 2.00 This is the first public release of this manuscript within the research programme structure. The paper presents the design-oriented analysis linking translation drift to meaning infrastructure and governance implications. Cross-paper terminology has been harmonised, the unit-of-analysis statement has been standardised across the series, and reference architecture has been aligned. The positioning of AI as interpretive traceability support is consistent with the architectural and methodological papers. No empirical datasets are associated with this version. Version 2.01: consolidates the manuscript within the full research programme structure. Cross-paper terminology has been harmonised, titles and references have been aligned with the programme statement, and internal cross-references have been updated. No changes have been made to the formal decision-learning architecture, measurement logic, boundary conditions, or theoretical claims. Empirical studies, measurement instruments, and field applications are in preparation and will be released in subsequent linked records.