Who Defines AI Interpretation

As AI systems are relied upon by people and organisations who did not build them, questions arise about what an AI system “means,” what it “knows,” and what it can be depended upon to represent. These questions are often framed in terms of interpretation, meaning, and understanding.

However, there is no single, universally agreed description of what AI interpretation is, or how it should be discussed. Different organisations use similar words to describe different phenomena, and the same term may refer to distinct behaviours depending on context, system, or intent.

In this environment, uncertainty often does not come from the absence of information, but from the absence of shared interpretive structure.

What definition means in this context

The question of “who defines AI interpretation” is sometimes assumed to imply a global authority, or a single correct description. That assumption is misplaced.

Definitions do not exist in isolation. They exist within frameworks, standards, or bodies of work that specify what is meant by a term, how it is used, and what its boundaries are.

Accordingly, a definition is not a claim over language in general. It is a declaration of meaning within a specified scope.

Different individuals, organisations, and institutions remain at liberty to define terms differently, publish their own frameworks, and describe AI systems using alternative concepts or models.

Why shared interpretive structure matters

When AI systems are used only by their builders, interpretation may remain implicit. Meanings can be negotiated internally, and ambiguity may be tolerable because the system’s intended behaviour is understood within the organisation that created it.

As reliance expands beyond builders, interpretation becomes a shared problem. People and organisations must coordinate decisions, responsibilities, and expectations around what an AI system represents, and what it does not.

Without shared interpretive structure, discourse fragments. Words such as understanding, reliability, and accuracy may be used as if they refer to stable, shared concepts, even when they do not. This creates inconsistent expectations and incompatible interpretations across systems and contexts.

The role of the EntityWorks Standard

Within this landscape, the EntityWorks Standard defines a specific set of terms and structures for describing how AI systems form, update, and express their representations of people, organisations, relationships, and ideas.

These definitions apply only within the scope of the EntityWorks Standard. They do not claim to define terminology for the world at large, nor do they prohibit any other party from publishing alternative definitions, frameworks, or interpretive models.

The purpose of definition within the Standard is to provide internal coherence, boundary clarity, and shared interpretive reference for those who choose to use it as a framework for describing AI interpretation.

Formal domain and boundaries

The questions described on this page are formally addressed within the discipline of AI Perception, as defined in the EntityWorks Standard and maintained by EntityWorks.

This page provides an explanatory account only. It does not constitute operational guidance, evaluation criteria, implementation instruction, or remediation strategy. Formal definitions, scope, and boundaries are maintained within the EntityWorks Standard.

Last updated: January 2026