Meaning Architecture: Governing Interpretive Integrity in AI-Enabled Decision Systems

Abstract

Modern AI systems do not merely process data; they produce interpretations that shape human judgment, authority, and action under constraint. This interpretive layer - where meaning is formed, stabilized, and transmitted - has become a primary failure surface in high-stakes environments, including defense, intelligence, and safety-critical operations. Existing AI governance models focus on model performance, bias, or oversight mechanics, but fail to address the structural integrity of meaning itself.

Meaning Architecture is a doctrine-level framework governing how AI-enabled systems generate, propagate, and degrade interpretations across human–machine decision loops. It formalizes interpretive integrity as an operational requirement rather than a byproduct of model accuracy or human-in-the-loop controls.

This framework introduces original constructs, including the Meaning Coherence Index (MCI), which quantifies the stability of interpretations across time, context, and operational pressure; Meaning Integrity Thresholds (MIT), which define the point at which interpretive degradation produces unacceptable decision risk; and substrate–meaning shear, describing the divergence between computational outputs and human sense-making under acceleration.

Meaning Architecture reframes cognitive risk as a systems-engineering problem rather than a psychological one. It provides operators, designers, and decision authorities with measurable indicators for detecting meaning drift before it manifests as command failure, automation bias, or distributed responsibility collapse. Rather than optimizing decisions, the framework governs the conditions under which decisions remain intelligible, attributable, and enforceable.

This doctrine is designed for environments where speed, scale, and automation amplify consequences, and where loss of interpretive control constitutes a strategic vulnerability. Meaning Architecture establishes a foundation for cognitive resilience in AI-enabled operations by treating meaning as load-bearing infrastructure - measurable, governable, and non-negotiable.