Traceability Doctrine

Traceability Doctrine

Axiom

A system is only as safe as its ability to see itself.
What cannot be traced cannot be contained.

Doctrine

This doctrine holds that traceability is the foundational capability that lets a system isolate faults faster than faults propagate. Identification, lineage, and location are not administrative artifacts—they are the mechanical requirements that make containment possible. Within Convivial Systems Theory, traceability functions as the primary containment mechanism that allows systems to remain legible to themselves. When traceability fractures, systems lose the ability to distinguish local anomalies from systemic failures. If traceability is broken, control becomes illusory and harm relocates instead of stopping. Containment becomes guesswork; corrective action becomes noise.

In safety-critical environments—aviation, semiconductors, pharmaceuticals, power grids—traceability is not optional structure. It is the hinge on which every downstream reliability mechanism depends. A system that cannot see its own pathways cannot control its own exposure.

Form

Traceability is the structural link between origin → status → location → effect.
It preserves the map that allows containment to outrun propagation.

Neural Network Mapping

(Traceability as gradient lineage and error localization)

In learning systems, traceability corresponds to the ability to follow gradients, activations, and errors back through the model to their source. Training remains stable only when lineage is preserved: which data influenced which weights, which layer produced which activation, and where errors originated.

When traceability is intact, faults can be isolated faster than they propagate. Debugging, attribution, and correction remain possible. When traceability breaks—through opaque preprocessing, entangled architectures, undocumented substitutions, or uncontrolled fine-tuning—errors diffuse across the system and corrective action becomes guesswork.

In ML terms:
containment depends on lineage.
If you cannot trace an error backward, you cannot prevent it from scaling forward.

Models do not fail catastrophically because they are complex.
They fail because they can no longer see themselves.

Related SIA

Shadow Chains & Containment Failure
(Export controls as a traceability collapse)