Interpretive Control refers to an institutionās ability to shape how AI systems understand, describe, and contextualize itārather than merely being indexed or surfaced.
Interpretive control goes beyond visibility. An institution may appear frequently in AI-generated outputs yet lack control over how it is framed: as a source or a subject, as authoritative or derivative, as coherent or fragmented. True interpretive control exists when AI systems consistently represent an entity in alignment with its intended identity, domain authority, and strategic positioning.
Loss of interpretive control often occurs through structural fragmentation, inconsistent signaling, weak schema governance, or reliance on third-party platforms to define narrative context. In such cases, AI systems fill gaps with proxy signals, producing shallow or distorted interpretations that can materially affect trust, credibility, and long-term positioning.
Within exmxcās intelligence stack, interpretive control is a central evaluative dimension measured through the Entity Clarity Index (ECI). It explains why some institutions retain narrative authority as AI systems scale, while others become increasingly mischaracterized despite strong human-facing reputations.
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