Interpretive observability
Interpretive observability designates the capacity to measure, detect, and attribute interpretation variations produced by an AI system, in order to monitor the stability of a canonical truth and identify causes of gap, drift, or capture.
Without observability, governance remains declarative. With observability, it becomes testable: one no longer debates “impressions”, one tracks signals (canon-output gap, inertia, trail, remanence, authority conflicts, legitimate non-responses).
Definition
Interpretive observability is the set of mechanisms allowing to:
- monitor output fidelity to the canon over time;
- detect a drift (compliance, framing, source activation);
- attribute a variation to a probable cause (activated source, neighborhood, response conditions, model, context);
- produce evidence (traces, reports, metrics) that is enforceable.
Interpretive observability does not describe the model’s internal mechanics. It describes what is necessary to govern outputs: inputs, conditions, sources, decisions, and observed effects.
