Explainable AI · Trust by Design

Every Decision is Traceable

ArcaQ's explainability architecture ensures every AI decision can be audited, verified, and explained. From query to response, every step is logged with complete provenance tracking.

Decision Audit Trail Architecture

Audit Flow: Query → Response

📝

Query Logged

Timestamp, user, context

🔍

SPARQL Generated

Query plan stored

📊

Facts Retrieved

Provenance URIs

🛡️

SCAG Filtered

Rule traces logged

✅

Response + Audit

Full chain linked

📌 Fact Provenance Tracking

Every fact in the Knowledge Graph has a provenance URI containing:

Source document URI
Extraction timestamp
Validator identity (Refinery Agent)
Confidence score (0.0 - 1.0)
Version history

⚡ CAG Reasoning Chain

Certified Augmented Generation ensures deterministic paths:

Query: "What is Q3 revenue?"
  ↓
SPARQL: SELECT ?value WHERE { 
  :Company :hasRevenue ?r .
  ?r :quarter "Q3" ; :value ?value 
}
  ↓
Result: [{ value: "$4.2M" }]
  ↓
Provenance: doc:annual-report-2025#line-142
  ↓
Response: "Q3 revenue was $4.2M"
  → Source: Annual Report 2025, Line 142
  → Confidence: 1.0
  → Audit ID: aud_8f3k2m1

🛡️ SCAG Filter Trace

4-layer semantic filter with full trace logging:

Legal: GDPR Article 17 applied
Hierarchical: Manager-level access verified
Cultural: Morocco business norms applied
Intangible: Reputation risk assessed

📤 Audit Export Formats

Export complete audit trails for compliance:

JSON-LD (machine-readable)
PDF (human-readable report)
CSV (data analysis)
RDF/Turtle (knowledge graph)
SIEM integration (security)

Why Explainability Matters

⚖️

Regulatory Compliance

Meet GDPR Article 22, EU AI Act, and SOC 2 requirements for explainable automated decisions

🔒

Litigation Defense

Complete audit trails provide evidence for disputed decisions in legal proceedings

🎯

Trust Building

Stakeholders trust AI decisions they can understand and verify

See Explainability in Action

Schedule a demo to see how ArcaQ's audit trail works with your data.

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