Smart Africa EOI · Controlled Synthetic Demonstrator

AI Trust Engine — Model & Assessment Report

Extensive evaluator-facing report generated from current demonstrator evidence.

Report classTechnical

Assessments15

EvidenceControlled

BoundaryAdvisory

1. Model registry

{
  "generated_at": "2026-09-15T14:54:10.439721+00:00",
  "governance": {
    "evidence_required_for_promotion": true,
    "institution_retains_final_decision": true,
    "model_changes_require_version_increment": true
  },
  "models": [
    {
      "explainability_required": true,
      "lifecycle_state": "ACTIVE_DEMO",
      "model_id": "TRUST-BASELINE",
      "model_version": "TRUST-BASELINE-0.1.0",
      "policy_decision_embedded_in_model": false,
      "production_approved": false,
      "status": "DEMONSTRATOR_ONLY"
    }
  ],
  "stage": "13"
}

2. Model usage

3. Output portfolio

4. Governance interpretation

Model output is analytical, not authorization.
Explainability and policy remain separate downstream concerns.
Production use requires institution-specific validation and monitoring.

Technical Manual

AI Trust Engine — Model & Assessment Report

/reports/ai-model/ · Contextual Technical Handbook V2.1

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Purpose Why It Exists Architecture Responsibilities Inputs Processing Outputs Interpretation Technical Notes Failure Modes Checklist Evidence Governance Walkthrough Acceptance Related

1. Page Purpose

The Reporting subsystem converts controlled evidence into governed Web and PDF artifacts for evaluators, technical reviewers and decision makers.

Reports are not independent narratives. They are generated from the same validated assessment, model, policy, telemetry, conformance and audit evidence used elsewhere in the demonstrator.

2. Why This Capability Exists

AI Trust Engine — Model & Assessment Report exists because a trustworthy cross-system payment assessment platform must make each material responsibility explicit. A page should not merely display information; it should show which system capability produced the information, what evidence was used, what versioned component was involved and what the downstream consequence is.

The TRUST design deliberately separates integration, normalization, evidence derivation, AI inference, explanation, policy, audit and institutional authority. This prevents one opaque component from silently performing multiple responsibilities and makes the overall result easier to reproduce, test and govern.

3. Where This Page Fits

The page sits within the end-to-end TRUST SDK assessment lifecycle. Upstream components provide controlled transaction context and downstream components consume the resulting evidence. The exact stage differs by page, but the architectural rule remains constant: data and responsibility move forward through explicit boundaries rather than through hidden coupling.

The final operational decision remains outside the TRUST analytical chain. TRUST can advise, explain and record evidence, but the participating institution retains final authority over its transaction.

4. Functional Responsibilities

Present the evaluator-facing capability represented by AI Trust Engine — Model & Assessment Report in a form that can be inspected and understood.
Expose the relevant operational, technical or evidential information without mixing unrelated component responsibilities.
Preserve linkage to upstream and downstream stages so an evaluator can follow the complete chain.
Retain version, provenance and decision-boundary information wherever the capability influences trust, risk or policy interpretation.
Fail safely when source evidence is missing, invalid or unsupported instead of inventing a result.

5. Inputs

Validated demonstrator evidence appropriate to the capability.
Page-specific runtime, integration, model, policy, telemetry or audit context.
Versioned implementation metadata needed for reproducibility.
Transaction, assessment or correlation identifiers where relevant.
Explicit missing-value or unsupported-data indicators where evidence is incomplete.

6. Processing Logic

Load only the controlled evidence required by the capability.
Validate that the required source evidence is present and structurally usable.
Transform the evidence into human-readable and machine-consistent outputs.
Preserve identifiers, version information and provenance across the transformation.
Expose failure or insufficiency explicitly rather than fabricating missing evidence.
Hand the resulting output to the next governed stage.

7. Outputs

Evaluator-facing interpretation of the capability.
Status, metrics, evidence, explanations, reports or controls appropriate to the page.
Version/provenance information where applicable.
Contextual links into related TRUST SDK capabilities.
A result that remains inside the defined institutional decision boundary.

8. Data & Evidence Interpretation

Executive reports summarize the portfolio; transaction reports preserve assessment-level detail; institution reports compare ecosystem-specific outcomes; technical and evidential reports explain implementation and provenance.

Embedded graphs are evidence-derived visualizations. They should never contain invented performance figures.

SHA256 values in the report registry support artifact integrity and provenance.

9. Technical Notes

Web and PDF versions share the same report model.
Inline SVG is used for charts so the graphics remain deterministic and render in both browser and PDF outputs.
The report registry records report ID, category, generation time, routes and artifact hashes.

10. Failure Modes & Safe Failure

Required evidence missing: the capability should report insufficiency or a controlled error rather than infer unsupported facts.
Version mismatch: the request or result should be rejected, quarantined or clearly marked until compatibility is restored.
Unsupported institution-specific value: the adapter or normalization layer should preserve the unsupported state rather than silently remap it.
Downstream service unavailable: the current stage should fail safely and retain enough context for diagnosis.
Conflicting evidence: the system should expose the inconsistency for review instead of hiding it.

11. Evaluator Verification Checklist

Evaluator question	Expected evidence
Is the capability clearly bounded?	The page and manual should state what the component does and what it explicitly does not do.
Is source evidence visible?	The capability should identify or link to the evidence used to produce the displayed result.
Is version information available?	Model, SDK, policy, adapter or implementation version should be available where relevant.
Can the result be reconstructed?	Identifiers and upstream/downstream evidence should make replay or reconstruction possible.
Does the component fail safely?	Missing or unsupported evidence must not be converted into invented facts.
Is institutional authority preserved?	TRUST may advise, but final transaction authority remains with the institution.

12. Evidence Sources

Page-visible runtime or assessment evidence.
Related technical and evidence-chain pages.
Model / policy / SDK registry information where applicable.
Audit references or telemetry where applicable.
Web/PDF reporting artifacts generated from the same controlled evidence.

13. Governance & Decision Boundary

TRUST is an advisory assessment capability. It does not independently authorize, settle or block a payment.

The participating institution retains final transaction authority and remains responsible for institution-specific compliance, operational policy and production controls.

The demonstrator uses synthetic or controlled evidence and should not be described as production certification.

14. Example Evaluator Walkthrough

Start from the page's principal status or result.
Identify the transaction, assessment, component or evidence source behind that result.
Follow the related contextual page to inspect the upstream evidence.
Verify the component/version that generated the output.
Follow the downstream path to explanation, policy, audit or report evidence as applicable.
Confirm that the displayed recommendation remains advisory and that the institution remains the final decision maker.

15. Acceptance / Readiness Criteria

The page loads successfully and exposes the intended capability.
Displayed values reconcile with controlled evidence.
Relevant version/provenance information is inspectable.
Contextual navigation reaches upstream/downstream stages.
Failure states are explicit and non-fabricating.
Institutional decision authority is clearly preserved.

16. Related Page Sections / Continue Reading

1. Model registry
2. Model usage
3. Output portfolio
4. Governance interpretation