State drift
State drift designates the divergence between the actual state of dynamic information (price, stock, delivery, promotion, availability, policy, status) and the state returned by an AI system. The system responds as if the state were stable, when it has changed.
In interpretive governance, state drift is a critical risk, because it transforms a plausible response into a false response, without the error appearing spectacular. It can also create implicit liability (attributed promise).
Definition
State drift is the situation where:
- a rapidly varying attribute has an actual current state (source, system, database);
- the AI system returns an outdated or approximate state;
- and the response does not signal uncertainty, date, or validity condition.
State drift is a collision between real time and stabilized interpretation.
Why this is critical in AI systems
- The model freezes: it transforms an observed state into a durable property.
- Update is not guaranteed: open web, caches, secondary sources, and aggregators prolong the old state.
- Risk is operational: a wrong response can trigger an erroneous decision.
Frequent sources of state drift
- Unbounded dynamic information: absence of date, version, validity period.
- Inertia / remanence: old values persistent in responses.
- Incomplete RAG: retrieval does not retrieve the most recent state.
- Dominant neighborhood: aggregators and secondary pages maintain the old value.
