Evaluator Context · Explainability Engine
EXPLAIN — Reason Codes
The explainability stage derives human-readable reason codes and interpretive narrative from the actual evidence used by the AI assessment. It is governed by a strict design rule: explanations must come from the real model evidence and must not be invented after the fact.
EXPLAIN — Reason Codes
Expanded evaluator context
Professional interpretation
Purpose in the demonstrator
This stage allows non-technical reviewers to understand why a result was produced. It is essential for institutional trust, audit review and responsible AI demonstration.
Operational meaning
Evidence alignmentDisplayed reasons must correspond to applied evidence indicators.
Reason registryReason codes are standardized and reusable across scenarios.
Human readabilityTechnical evidence is translated into evaluator-friendly explanation.
Inputs and outputs
| Element | Meaning in this demonstrator |
|---|---|
| AI assessment | Trust, risk and confidence produced by the AI stage. |
| Applied evidence | Model-relevant contextual indicators linked to the assessment. |
| Reason codes | Governed textual explanations suitable for institutional review. |
| Output | Transparent interpretation used by the evaluator and policy stage. |
Evaluator questions this page answers
- Why did this transaction score low or high?
- Can the reasons be traced back to actual model evidence?
- How does the portal avoid post-hoc storytelling?
- What makes the demonstrator’s explainability credible?
How this stage fits in the end-to-end chain
1. AI TRUSTAssessment produced.
2. EXPLAINEvidence-aligned reasons generated.
3. POLICYRecommendation is informed by trust, risk, confidence and explanation context.
4. AUDITExplanation trace remains reviewable.
Governance and interpretation notes
This page is an evaluator-facing contextual explanation of the demonstrator stage. It explains the business role, the technical contribution, the governance boundary and the expected interpretation of the displayed output. It is designed to make the demonstration credible, understandable and professionally reviewable without exposing raw internal JSON on-screen.
Explainability Evidence Chain
This visual shows how model-applied evidence becomes feature contribution, reason codes and human-readable explanation.
The demonstrator rule is explicit: explanations must reflect actual evidence used by the assessment.