AKEL – AI Knowledge Extraction Layer

 Version: 0.9.70 Last Updated: December 21, 2025 Status: CORRECTED - Automation Philosophy Consistent AKEL is FactHarbor's automated intelligence subsystem. Its purpose is to reduce human workload, enhance consistency, and enable scalable knowledge processing. AKEL outputs are marked with AuthorType = AI and published according to risk-based policies (see Publication Modes below). AKEL operates in two modes:

• Single-node mode (POC & Beta 0)
• Federated multi-node mode (Release 1.0+) == 1. Core Philosophy: Automation First == V0.9.50+ Philosophy Shift: FactHarbor uses "Improve the system, not the data" approach: * ✅ Automated Publication: AI-generated content publishes immediately after passing quality gates
• ✅ Quality Gates: Automated checks (not human approval)
• ✅ Sampling Audits: Humans analyze patterns for system improvement (not individual approval)
• ❌ NO approval workflows: No review queues, no moderator gatekeeping for content quality
• ❌ NO manual fixes: If output is wrong, improve the algorithm/prompts Why This Matters: Traditional approach: Human reviews every output → Bottleneck, inconsistent FactHarbor approach: Automated quality gates + pattern-based improvement → Scalable, consistent == 2. Publication Modes == V0.9.70 CLARIFICATION: FactHarbor uses TWO publication modes (not three): === Mode 1: Draft-Only === Status: Not visible to public When Used:
• Quality gates failed
• Confidence below threshold
• Structural integrity issues
• Insufficient evidence What Happens:
• Content remains private
• System logs failure reasons
• Prompts/algorithms improved based on patterns
• Content may be re-processed after improvements NOT "pending human approval" - it's blocked because it doesn't meet automated quality standards. === Mode 2: AI-Generated (Public) === Status: Published and visible to all users When Used:
• Quality gates passed
• Confidence ≥ threshold
• Meets structural requirements
• Sufficient evidence found Includes:
• Confidence score displayed (0-100%)
• Risk tier badge (A/B/C)
• Quality indicators
• Clear "AI-Generated" labeling
• Sampling audit status Labels by Risk Tier:
• Tier A (High Risk): "⚠️ AI-Generated - High Impact Topic - Seek Professional Advice"
• Tier B (Medium Risk): "🤖 AI-Generated - May Contain Errors"
• Tier C (Low Risk): "🤖 AI-Generated" === REMOVED: "Mode 3: Human-Reviewed" === V0.9.50 Decision: No centralized approval workflow. Rationale:
• Defeats automation purpose
• Creates bottleneck
• Inconsistent quality
• Not scalable What Replaced It:
• Better quality gates
• Sampling audits for system improvement
• Transparent confidence scoring
• Risk-based warnings == 3. Risk Tiers (A/B/C) == Risk classification determines WARNING LABELS and AUDIT FREQUENCY, NOT approval requirements. === Tier A: High-Stakes Claims === Examples: Medical advice, legal interpretations, financial recommendations, safety information Impact:
• ✅ Publish immediately (if passes gates)
• ✅ Prominent warning labels
• ✅ Higher sampling audit frequency (50% audited)
• ✅ Explicit disclaimers ("Seek professional advice")
• ❌ NOT held for moderator approval Philosophy: Publish with strong warnings, monitor closely === Tier B: Moderate-Stakes Claims === Examples: Political claims, controversial topics, scientific debates Impact:
• ✅ Publish immediately (if passes gates)
• ✅ Standard warning labels
• ✅ Medium sampling audit frequency (20% audited)
• ❌ NOT held for moderator approval === Tier C: Low-Stakes Claims === Examples: Entertainment facts, sports statistics, general knowledge Impact:
• ✅ Publish immediately (if passes gates)
• ✅ Minimal warning labels
• ✅ Low sampling audit frequency (5% audited) == 4. Quality Gates (Automated, Not Human) == All AI-generated content must pass these AUTOMATED checks before publication: === Gate 1: Source Quality === Automated Checks:
• Primary sources identified and accessible
• Source reliability scored against database
• Citation completeness verified
• Publication dates checked
• Author credentials validated (where applicable) If Failed: Block publication, log pattern, improve source detection === Gate 2: Contradiction Search (MANDATORY) === The system MUST actively search for: * Counter-evidence – Rebuttals, conflicting results, contradictory studies
• Reservations – Caveats, limitations, boundary conditions
• Alternative interpretations – Different framings, definitions
• Bubble detection – Echo chambers, ideologically isolated sources Search Coverage Requirements:
• Academic literature (BOTH supporting AND opposing views)
• Diverse media across political/ideological perspectives
• Official contradictions (retractions, corrections, amendments)
• Cross-cultural and international perspectives Search Must Avoid Algorithmic Bubbles:
• Deliberately seek opposing viewpoints
• Check for echo chamber patterns
• Identify tribal source clustering
• Flag artificially constrained search space
• Verify diversity of perspectives Outcomes:
• Strong counter-evidence → Auto-escalate to Tier B or draft-only
• Significant uncertainty → Require uncertainty disclosure in verdict
• Bubble indicators → Flag for sampling audit
• Limited perspective diversity → Expand search or flag If Failed: Block publication, improve search algorithms === Gate 3: Uncertainty Quantification === Automated Checks:
• Confidence scores calculated for all claims and verdicts
• Limitations explicitly stated
• Data gaps identified and disclosed
• Strength of evidence assessed
• Alternative scenarios considered If Failed: Block publication, improve confidence scoring === Gate 4: Structural Integrity === Automated Checks:
• No hallucinations detected (fact-checking against sources)
• Logic chain valid and traceable
• References accessible and verifiable
• No circular reasoning
• Premises clearly stated If Failed: Block publication, improve hallucination detection CRITICAL: If any gate fails:
• ✅ Content remains in draft-only mode
• ✅ Failure reason logged
• ✅ Failure patterns analyzed for system improvement
• ❌ NOT "sent for human review"
• ❌ NOT "manually overridden" Philosophy: Fix the system that generated bad output, don't manually fix individual outputs. == 5. Sampling Audit System == Purpose: Improve the system through pattern analysis (NOT approve individual outputs) === 5.1 How Sampling Works === Stratified Sampling Strategy: Audits prioritize:
• Risk tier (Tier A: 50%, Tier B: 20%, Tier C: 5%)
• AI confidence score (low confidence → higher sampling rate)
• Traffic and engagement (high-visibility content audited more)
• Novelty (new claim types, new domains, emerging topics)
• Disagreement signals (user flags, contradiction alerts, community reports) NOT: Review queue for approval IS: Statistical sampling for quality monitoring === 5.2 Audit Process === 1. System selects content for audit based on sampling strategy
  2. Human auditor reviews AI-generated content against quality standards
  3. Auditor validates or identifies issues: * Claim extraction accuracy * Scenario appropriateness * Evidence relevance and interpretation * Verdict reasoning * Contradiction search completeness
  4. Audit outcome recorded (pass/fail + detailed feedback)
  5. Failed audits trigger: * Analysis of failure pattern * System improvement tasks * Algorithm/prompt adjustments
  6. Audit results feed back into system improvement CRITICAL: Auditors analyze PATTERNS, not fix individual outputs. === 5.3 Feedback Loop (Continuous Improvement) === Audit outcomes systematically improve: * Query templates – Refined based on missed evidence patterns
• Retrieval source weights – Adjusted for accuracy and reliability
• Contradiction detection heuristics – Enhanced to catch missed counter-evidence
• Model prompts and extraction rules – Tuned for better claim extraction
• Risk tier assignments – Recalibrated based on error patterns
• Bubble detection algorithms – Improved to identify echo chambers Philosophy: "Improve the system, not the data" === 5.4 Audit Transparency === Publicly Published:
• Audit statistics (monthly)
• Accuracy rates by risk tier
• System improvements made
• Aggregate audit performance Enables:
• Public accountability
• System trust
• Continuous improvement visibility == 6. Human Intervention Criteria == From Organisation.Decision-Processes: LEGITIMATE reasons to intervene: * ✅ AKEL explicitly flags item for sampling audit
• ✅ System metrics show performance degradation
• ✅ Legal/safety issue requires immediate action
• ✅ User reports reveal systematic bias pattern ILLEGITIMATE reasons (system improvement needed instead): * ❌ "I disagree with this verdict" → Improve algorithm
• ❌ "This source should rank higher" → Improve scoring rules
• ❌ "Manual quality gate before publication" → Defeats purpose of automation
• ❌ "I know better than the algorithm" → Then improve the algorithm Philosophy: If you disagree with output, improve the system that generated it. == 7. Architecture Overview == === POC Architecture (POC1, POC2) === Simple, Single-Call Approach: ```
  User submits article/claim ↓
  Single AI API call ↓
  Returns complete analysis ↓
  Quality gates validate ↓
  PASS → Publish (Mode 2)
  FAIL → Block (Mode 1)
  ``` Components in Single Call:

1. Extract 3-5 factual claims
   2. For each claim: verdict + confidence + risk tier + reasoning
   3. Generate analysis summary
   4. Generate article summary
   5. Run basic quality checks Processing Time: 10-18 seconds Advantages: Simple, fast POC development, proves AI capability Limitations: No component reusability, all-or-nothing === Full System Architecture (Beta 0, Release 1.0) === Multi-Component Pipeline: ```
   AKEL Orchestrator
   ├── Claim Extractor
   ├── Claim Classifier (with risk tier assignment)
   ├── Scenario Generator
   ├── Evidence Summarizer
   ├── Contradiction Detector
   ├── Quality Gate Validator
   ├── Audit Sampling Scheduler
   └── Federation Sync Adapter (Release 1.0+)
   ``` Processing:

• Parallel processing where possible
• Separate component calls
• Quality gates between phases
• Audit sampling selection
• Cross-node coordination (federated mode) Processing Time: 10-30 seconds (full pipeline) === Evolution Path === POC1: Single prompt → Prove concept POC2: Add scenario component → Test full pipeline Beta 0: Multi-component AKEL → Production architecture Release 1.0: Full AKEL + Federation → Scale == 8. AKEL and Federation == In Release 1.0+, AKEL participates in cross-node knowledge alignment: * Shares embeddings
• Exchanges canonicalized claim forms
• Exchanges scenario templates
• Sends + receives contradiction alerts
• Shares audit findings (with privacy controls)
• Never shares model weights
• Never overrides local governance Nodes may choose trust levels for AKEL-related data:
• Trusted nodes: auto-merge embeddings + templates
• Neutral nodes: require additional verification
• Untrusted nodes: fully manual import == 9. POC Behavior == The POC explicitly demonstrates AI-generated content publication: * ✅ Produces public AI-generated output (Mode 2)
• ✅ No human data sources required
• ✅ No human approval gate
• ✅ Clear "AI-Generated - POC/Demo" labeling
• ✅ All quality gates active (including contradiction search)
• ✅ Users understand this demonstrates AI reasoning capabilities
• ✅ Risk tier classification shown (demo purposes) Philosophy Validation: POC proves automation-first approach works. == 10. Related Pages == * Automation
• Requirements (Roles)
• Workflows
• Governance
• Decision Processes V0.9.70 CHANGES:
  - ❌ REMOVED: Section "Human Review Workflow (Mode 3 Publication)"
  - ❌ REMOVED: All references to "Mode 3"
  - ❌ REMOVED: "Human review required before publication"
  - ✅ CLARIFIED: Two modes only (AI-Generated / Draft-Only)
  - ✅ CLARIFIED: Quality gate failures → Block + improve system
  - ✅ CLARIFIED: Sampling audits for improvement, NOT approval
  - ✅ CLARIFIED: Risk tiers affect warnings/audits, NOT approval gates
  - ✅ ENHANCED: Gate 2 (Contradiction Search) specification
  - ✅ ADDED: Clear human intervention criteria
  - ✅ ADDED: Detailed audit system explanation