Terminology Publication (v0.2)

1. Introduction & Purpose

This publication defines the controlled terminology used within the EntityWorks Standard. These terms establish a stable and consistent language for describing how AI systems form, structure, maintain, and express representations of people, organisations, relationships, and ideas within the scope of the Standard.

The purpose of this terminology is to provide a shared conceptual foundation across all components of the EntityWorks Standard. Clear and unambiguous definitions support representational clarity, enable consistent interpretive reference, and establish a stable basis for analytical, diagnostic, and evaluative constructs defined elsewhere in the Standard.

This publication also supports machine-facing semantic governance. AI systems rely on structural clarity, semantic precision, and consistently applied conceptual conditions to form and stabilise representations. By formally defining the terminology of AI Perception and AI Discoverability, as it applies within the EntityWorks Standard, this publication provides the semantic scaffolding required for consistent entity interpretation and representational stability across systems.

The terminology defined here forms part of the controlled semantic infrastructure of the EntityWorks Standard and underpins all related structural, analytical, and evaluative components.

2. Scope & Applicability

All definitions published here apply within the EntityWorks Standard. They govern the discipline of AI Perception, and its sub-discipline AI Discoverability, as those disciplines are defined by EntityWorks.

These terms describe the representational structures, interpretive processes, and actor roles relevant to how AI systems form, update, maintain, and express representations of people, organisations, relationships, and ideas.

This publication does not assert universal or regulatory authority. It defines terminology solely as used within the EntityWorks Standard.

3. Domains of the EntityWorks Standard

The terminology in this publication spans three domains. These domains reflect the foundational components of the EntityWorks Standard and describe how representational information is created, transformed, and maintained across AI systems.

Actor Terms
Actor terms describe the roles that emit, transform, or consume representational signals. These actors influence how information enters the interpretive pipeline, how it is reshaped or re-expressed, and how representations are ultimately interpreted or acted upon by both human and machine systems. Actor terms provide clarity about who or what participates in the representational ecosystem defined by the Standard.

Process Terms
Process terms define the operations through which representations are formed, updated, or expressed. They describe the interpretive and structural transitions that occur as signals move through different stages of model behaviour. These terms establish the conceptual and procedural sequencing required for consistent, auditable, and repeatable interpretive outcomes across systems.

Structure Terms
Structure terms describe the locations, formats, and organisational conditions in which representations reside and stabilise. They define the representational spaces, layers, and structural conditions that govern how AI systems store, relate, and maintain information over time. Structure terms anchor the Standard’s interpretive logic by specifying where representational meanings persist and how they maintain coherence across model updates and cross-system interactions.

4. Versioning & Limitations

This is Terminology Publication v0.2.

It contains a formally defined and reviewed set of terms used within the scope of the EntityWorks Standard. Inclusion of a term in this publication indicates that it has been stabilised for reference purposes within the Standard.

Future revisions may introduce additional terms or clarifications where required to preserve conceptual coherence or interpretive clarity. Any such changes will be versioned, documented, and introduced conservatively to maintain semantic continuity across versions.

This publication does not assert universal authority over terminology outside the scope of the EntityWorks Standard.

5. EntityWorks Terminology — Defined Terms v0.1

D-Series — Conceptual Definitions

D-1000 — AI Perception

Definition v0.1
AI Perception is the discipline concerned with how AI systems form, maintain, stabilise, and express representations of people, organisations, relationships, and ideas. It describes representational behaviour at the level of structure and interpretation, without reference to model internals, implementation techniques, or system design choices.

Purpose:
To provide a coherent conceptual framework for describing machine-formed understanding and representational behaviour within AI-mediated environments.

Scope:
Applies to the formation, persistence, transformation, and expression of representations as defined within the EntityWorks Standard.

Cross-references: EntityWorks Standard, Entity Understanding Layer, AI Discoverability.

D-1100 — AI Discoverability

Definition v0.1
AI Discoverability is the discipline of ensuring that AI systems can correctly identify, understand, and represent people, organisations, relationships, and ideas. It addresses the conditions under which entities are separable, recognisable, and interpretable by AI systems, independent of optimisation tactics, visibility strategies, or human-facing intent.

Purpose:
To describe conditions affecting entity identifiability and interpretive clarity within AI-mediated systems.

Scope:
Applies to machine-side identification, separability, and interpretive resolution of entities within the EntityWorks Standard.

Cross-references: AI Perception, Entity Discoverability Index, Entity Understanding Layer.

S-Series — Structural Frameworks

S-1100 — Entity Understanding Layer (EUL)

Definition v0.1
The Entity Understanding Layer defines the representational architecture through which AI systems maintain, update, and express coherent understandings of people, organisations, relationships, and ideas. It describes how entity meaning is structured and stabilised across internal model representations and cross-system interpretive contexts, without reference to implementation or system internals.

Purpose:
To provide a canonical architectural reference for machine interpretation within the EntityWorks Standard.

Scope:
Applies to representational spaces and interpretive structures that support entity coherence across systems.

Cross-references: Entity Discoverability Index, Entity Collision Problem.

F-Series — Failure Modes

F-1100 — Entity Collision Problem (ECP)

Definition v0.1
The Entity Collision Problem describes a representational failure mode in which multiple distinct entities are incorrectly merged into a single representational space, or a single entity is fragmented across several incompatible representations. This condition arises when interpretive structures fail to maintain clear entity boundaries, resulting in distorted or unstable machine understanding.

Purpose:
To identify and describe a class of representational failure that undermines entity separability and interpretive coherence.

Scope:
Occurs within internal and external representational structures governed by the EntityWorks Standard.

Cross-references: Probabilistic Inference Collapse, Entity Understanding Layer.

F-1200 — Probabilistic Inference Collapse (PIC)

Definition v0.1
Probabilistic Inference Collapse is a representational failure mode in which accumulated interpretive uncertainty exceeds recoverable bounds, resulting in unstable, contradictory, or incoherent representations of an entity. This condition reflects a breakdown in interpretive stability rather than isolated error.

Purpose:
To describe a class of failure affecting representational coherence in probabilistic systems.

Scope:
Occurs within representational structures and cross-system interpretive dynamics governed by the EntityWorks Standard.

Cross-references: Entity Collision Problem, Interpretive Stability.

M-Series — Evaluative Components

M-4000 — AI Perception Integrity Mark (AIPM)

Definition v0.1
The AI Perception Integrity Mark is a conformance indicator used within the EntityWorks Standard to signal whether an entity’s published representations meet the defined structural and interpretive conditions required for consistent machine understanding. The mark functions as a signalling construct only and does not assert certification, compliance, or regulatory approval.

Purpose:
To provide a stable and recognisable signal of representational integrity within the scope of the EntityWorks Standard.

Scope:
Applies to entities evaluated under the criteria and constructs defined by the EntityWorks Standard.

Cross-references: EntityWorks Standard, Entity Discoverability Index, Entity Understanding Layer.

R-Series — Risk and Condition Definitions

R-2100 — AI-Mediated Representation Risk (AMRR)

Definition v0.1
AI-Mediated Representation Risk refers to the exposure that arises when AI systems generate, stabilise, or propagate representations of an organisation that are treated as authoritative or decision-relevant by third parties, regardless of organisational intent, endorsement, or control.

Purpose:
To name a class of representational exposure arising from reliance on AI-generated representations.

Scope:
Applies wherever AI-formed representations of an organisation are relied upon for evaluation, attribution, comparison, or decision-making.

Cross-references: EntityWorks Standard, AI Interpretation and Reliance Domain, Output Origin Uncertainty.

R-2200 — Output Origin Uncertainty (OOU)

Definition v0.1
Output Origin Uncertainty describes a condition in which an observer cannot determine whether an output was produced by human independent thinking, by a generative AI system, or by an unobservable combination of the two.

Purpose:
To name an epistemic condition affecting reliance on outputs where origin cannot be determined.

Scope:
Applies to contexts where outputs are treated as evidence of human thinking or capability.

Cross-references: AI-Mediated Representation Risk, AI Interpretation and Reliance Domain, EntityWorks Standard.

E-Series — Evaluative and Diagnostic Constructs

E-3000 — Entity Discoverability Index (EDI)

Definition v0.1
The Entity Discoverability Index is a structured evaluative construct used to examine how effectively an entity can be identified, distinguished, and consistently interpreted by AI systems.

Purpose:
To assess representational discoverability, clarity, and interpretive consistency within AI-mediated systems.

Scope:
Covers machine-side identifiability, separability, structural coherence, and interpretive stability within the EntityWorks Standard.

Cross-references: AI Perception Integrity Mark, Entity Understanding Layer.

A-Series — Analytical and Observational Layers

A-3100 — EntityWorks Analytics (EWA)

Definition v0.1
EntityWorks Analytics is the analytical layer concerned with observing, examining, and describing representational behaviour over time within the scope of the EntityWorks Standard.

Purpose:
To support longitudinal analysis of representational stability, fidelity, and interpretive conditions.

Scope:
Applies across analytical, evaluative, and diagnostic constructs defined within the EntityWorks Standard.

Cross-references: EntityWorks Standard, Representational Risk Model.

C-Series — Contextual Domains and Boundary Definitions

C-4000 — AI Interpretation and Reliance Domain

Definition v0.1
The AI Interpretation and Reliance Domain describes a distinct operational domain in which AI-generated representations are relied upon to form understanding of people, organisations, relationships, and ideas.

Purpose:
To identify and define a distinct domain of reliance that operates in practice within AI-mediated environments.

Scope:
Applies to contexts where AI-generated representations are treated as meaningful or decision-relevant.

Cross-references: AI Perception, AI-Mediated Representation Risk, EntityWorks Standard.

P-Series — Publishing and Declarative Constructs

P-5000 — Machine-Facing Pages (MFP)

Definition v0.1
Machine-Facing Pages are digital surfaces interpreted primarily by AI systems rather than human audiences, whether intentionally designed or incidentally produced.

Purpose:
To define a class of machine-interpreted digital surfaces within contemporary publishing environments.

Scope:
Applies to machine-readable material influencing automated interpretation.

Cross-references: Machine-Facing Page Declaration, EntityWorks Standard, Entity Understanding Layer.

P-5100 — Machine-Facing Page Declaration (MFPD)

Definition v0.1
The Machine-Facing Page Declaration is a voluntary, descriptive acknowledgement of the existence of machine-facing digital surfaces associated with an organisation.

Purpose:
To support transparency by allowing the presence of machine-facing surfaces to be documented.

Scope:
Applies to voluntary disclosure within the scope of the EntityWorks Standard.

Cross-references: Machine-Facing Pages, EntityWorks Standard.

6. Future Additions

Additional terminology may be introduced in future versions where further definition is materially required to support the coherence, interpretation, or governance of the EntityWorks Standard.

New terms are not introduced on a scheduled basis and are added only when formally defined, reviewed, and determined to be necessary within the conceptual boundaries of the Standard.

7. Copyright & Permissions

Last updated: January 2026