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EVIDE Boundary Model v2 – Graded Evidentiary States and External Defensibility

Version 2 – 27 April 2026 – 16:49 UTC – Extension of the initial formulation recorded on 27 April 2026

Previous version: EVIDE Boundary Model — First recorded formulation (27 April 2026)

Canonical Formulation

Layer 1 determines validity of attribution.
Layer 2 determines strength of attribution.

EVIDE Boundary Model — Two-Layer Architecture

The EVIDE Boundary Model defines the transition from system-internal evidence to externally attributable responsibility. This transition is not triggered by data completeness alone, but by structured responsibility conditions organized across two distinct levels.

Level 1 — Handoff Validity

Level 1 determines whether an object can cross the Layer 2 to Layer 3 boundary.

Conditions:

  • Behavioral completeness
  • Responsibility closure
  • Authority coherence

If these conditions are not satisfied simultaneously, the object is rejected at the boundary. Level 1 answers the question: can responsibility be attributed?

Level 2 — Evidentiary Strength Layer

Level 2 determines how strong the object remains once outside the originating system. This is not a gate, but a measure of evidentiary strength.

Conditions:

  • Interpretability — classification context stability
  • Reproducibility — trace continuity
  • Governance transparency — threshold reference clarity

Level 2 answers the question: is that responsibility defensible outside the system?

Key Distinction

An object may satisfy Level 1 and still present degraded evidentiary strength at Level 2. In legal and audit contexts, a passed-but-weak object is often more problematic than a rejected one, because it creates ambiguity rather than clarity.

Critical Insight

The EVIDE Boundary Model shifts AI governance from demonstrating what happened to establishing who is responsible and how defensible that responsibility remains once it leaves the originating system boundary.

Extended Validation — Graded Evidentiary States

Further validation of the EVIDE Boundary Model has introduced a third operational state beyond binary acceptance and rejection.

The model now distinguishes between three outcomes:

  • Rejected at Level 1 — attribution is not valid, object does not cross the boundary
  • Passed with full evidentiary strength — attribution is valid and fully reproducible
  • Passed with degraded evidentiary strength — attribution is valid but the responsibility chain is not fully reproducible outside the system

This extension confirms that Level 2 is not a secondary gate, but a structured measure of evidentiary strength.

The boundary therefore does not only control whether an object crosses, but also preserves how that object must be interpreted once it has crossed.

This allows the model to represent not only valid and invalid responsibility states, but also graded evidentiary conditions relevant for audit and dispute contexts.

Validation Reference

This formulation extends the initial version recorded on 27 April 2026.

The extended validation, including graded evidentiary states, is documented and certified as part of the EVIDE technical record.

Operational reference cases: TGTRACING × EVIDE boundary validation — April 2026, including full validation across rejection, full-strength, and degraded evidentiary states.

External validation surface:

CLARIXO reference-case summary


Explore the protocol infrastructure:

→ The </AI> Protocol
→ From decision to defensible structure: when supervision becomes evidence
→ Read the Public Technical Specification
→ Verify a CWC Code in the Public Registry
→ EVIDE – Evidentiary registry for digital content and decisions
→ CWC Registry Policy
→ Request an Official CWC Verification Code
→ AI Governance Documentation Framework
→ Implementation Guide: verifiable AI supervision
→ Oversight Bias: why human supervision can fail in AI systems
→ Decision Attestation Layer: the missing evidentiary layer in AI governance
→ AI Evidence Officer: proving human supervision in artificial intelligence systems
→ Evidentiary Layer in AI Governance
→ </AI> Protocol FAQ: questions and answers about the framework

Related reading:

→ AI Data Poisoning: The Attack No Antivirus Can Stop
→ Human in the loop: why saying there is human oversight is not enough
→ Real cases: when AI governance fails — and what should have been provable
→ AI Governance: when something has already gone wrong – forensic reconstruction and digital evidence

Work with us:

→ Legal Partners Network

Understanding the </AI> Protocol

If this is your first time here, this is a quick orientation.

The </AI> Protocol is not a single page. It is a structured system made of distinct layers.

In short:

Governance defines how decisions should happen.
The </AI> Protocol makes those decisions provable.