Interpretive smoothing
Interpretive smoothing designates an AI system’s tendency to erase specificities, nuances, exceptions, or paradoxes of a concept, in order to fit it into a standardized, more frequent, and easier-to-synthesize category.
Interpretive smoothing is a powerful impoverishment mechanism: it transforms specific thought into an “average” version, and can cause canon invisibilization, neighborhood contamination, or interpretive capture.
Definition
Interpretive smoothing is the fact that an AI system:
- reduces a specific concept to a generic form;
- suppresses important distinctions (conditions, perimeters, negations);
- reformulates in dominant, more frequent vocabulary;
- produces a “coherent” response, but less faithful to the canon.
Interpretive smoothing is not necessarily a factual hallucination. It is a structural distortion: meaning remains plausible, but constraints disappear.
Why this is critical in AI systems
- The model optimizes readability: it favors standard explanation forms.
- The model maximizes plausibility: it replaces edge cases with general rules.
- The model degrades governance: it erases precisely what makes a canon enforceable.
Common forms of smoothing
- Perimeter smoothing: suppression of limits (versions, jurisdictions, conditions).
- Negation smoothing: disappearance of “what this is not”.
- Responsibility smoothing: implicit attribution of promises, guarantees, or obligations.
- Terminological smoothing: replacement of canonical vocabulary by generic categories.
