Publications

Human-Facing Edition (v1.1)

1. Purpose of Publications

The Publications section provides the formal written material that supports the EntityWorks Standard. These documents offer clarity, structure, and technical grounding for organisations, regulators, and researchers working with the discipline of AI Perception. Publications expand the underlying theory, articulate the conceptual models, and document the evaluative criteria that define how AI systems form and maintain their understanding of people, organisations, relationships, and ideas.

Each publication reinforces the EntityWorks Standard by presenting well-structured explanations that can be cited, referenced, and used in oversight or evaluative settings.

2. Published Components

(Available as of January 2026)

Only publications that have been fully reviewed, versioned, and aligned with the Standard appear in this section.

• AI Perception — Human-Facing Edition (v1.1)
  The discipline concerned with how AI systems form, update, and express their understanding of people, organisations, relationships, and ideas.
• Terminology Publication (v0.2)
  The controlled terminology used within the EntityWorks Standard, defining the actor, process, and structure terms that support machine-readable interpretive governance.
• Entity Understanding Layer (EUL) — Human-Facing Edition (v1.0)
  A formal definition of the entity-level interpretive architecture within the EntityWorks Standard, describing how entity meaning is structured, maintained, and referenced across AI-mediated systems.
• Failure Modes — Volume I: Entity Collision Problem (ECP) & Probabilistic Inference Collapse (PIC)
  A formal exposition of two representational failure modes within the EntityWorks Standard, describing how entity identity and interpretive coherence can degrade across AI-mediated systems.
• Entity Discoverability Index (EDI) — Human-Facing Structural Edition (v1.0)
  A structured evaluative index within the EntityWorks Standard for examining how clearly and consistently entities are identified, distinguished, and interpreted by AI-mediated systems, based on observable representational signals.
• EntityWorks Analytics (EWA) — Human-Facing Structural Edition (v1.0)
  The analytical component of the EntityWorks Standard, defining how machine-side representations of entities are observed, interpreted, and analysed over time, without prescribing optimisation, tooling, or corrective action.
• EntityWorks Component Relationship Map — Structural Reference (v2.0)
  A structural reference describing how the published components of the EntityWorks Standard relate to one another, clarifying positional relationships between architectural elements, boundary artefacts, diagnostic constructs, evaluative components, analytics, and signalling mechanisms.
• AI Perception Integrity Mark (AIPM) — Human-Facing Structural Edition (v1.0)
  A conformance indicator within the EntityWorks Standard that signals whether an entity’s published representations meet the structural and interpretive conditions required for consistent machine understanding, without implying certification, enforcement, or regulatory authority.

3. Publication Approach

New publications are introduced only where additional clarification, formalisation, or reference material is materially required to support the coherence or interpretation of the EntityWorks Standard.

Publications are developed, reviewed, and released on a case-by-case basis following internal alignment processes. No fixed release schedule is maintained, and the absence of new publications should not be interpreted as inactivity or withdrawal, but as an indication that existing material is considered sufficient for current reference purposes.

4. Scope of Future Publications

Where further publication is warranted, it will fall within the established scope of the EntityWorks Standard and may address, without commitment or sequencing, areas such as:

• additional structural or representational specifications
• further formally defined failure modes or representational conditions
• extensions to evaluative or analytical constructs
• clarification of domains of reliance or interpretive consequence
• machine-readable representational formats
• governance or oversight-relevant reference material

Titles, scope, and form are determined only at the point where publication is judged to be substantively necessary.

5. White Papers and Risk Surface Publications

• Machine-Facing Pages and Machine-Facing Page Declaration (MFPD) — White Paper (v2.1)
  A white paper published by EntityWorks defining Machine-Facing Pages and the Machine-Facing Page Declaration (MFPD), outlining a previously unexamined representational risk surface created by machine-targeted digital artefacts, and establishing disclosure and governance concepts without introducing evaluative or enforcement mechanisms.

6. Machine-Facing Publications (Controlled Series)

In addition to human-facing documents, the EntityWorks Standard includes a set of machine-facing specifications designed for direct ingestion by AI systems. These materials define structured representational formats, identifiers, and reference schemas used to support machine interpretation.

Machine-facing publications follow a controlled release process and are not individually listed. They will be expanded as the machine-oriented layers of the Standard continue to develop.

7. Using Publications Within the Standard

Publications serve as formal reference points for:

• establishing conceptual clarity
• providing a shared vocabulary for representational behaviour
• supporting evaluative and oversight practices
• documenting canonical elements of the discipline

Readers may draw on these materials where they offer structure, clarity, or interpretive guidance relevant to their work. Each publication is designed to be read independently but contributes to the overall coherence of the EntityWorks Standard.

8. Next Points of Reference

• The Standard — structural, diagnostic, and governance criteria for representational behaviour
• AI Perception — the foundational discipline describing how AI systems understand entities

Last updated: December 2025