Automation

Automation in FactHarbor amplifies human capability but never replaces human oversight.
All automated outputs require human review before publication.

This chapter defines:

• What must remain human-only
• What AI (AKEL) can draft
• What can be fully automated
• How automation evolves through POC → Beta 0 → Release 1.0

Manual vs Automated Responsibilities

Human-Only Tasks

These require human judgment, ethics, or contextual interpretation:

• Definition of key terms in claims
• Approval or rejection of scenarios
• Interpretation of evidence in context
• Final verdict approval
• Governance decisions and dispute resolution
• High-risk domain oversight
• Ethical boundary decisions (especially medical, political, psychological)

Semi-Automated (AI Draft → Human Review)

AKEL can draft these, but humans must refine/approve:

• Scenario structures (definitions, assumptions, context)
• Evaluation methods
• Evidence relevance suggestions
• Reliability hints
• Verdict reasoning chains
• Uncertainty and limitations
• Scenario comparison explanations
• Suggestions for merging or splitting scenarios
• Draft public summaries

Fully Automated Structural Tasks

These require no human interpretation:

• Claim normalization
• Duplicate & cluster detection (vector embeddings)
• Evidence metadata extraction
• Basic reliability heuristics
• Contradiction detection
• Re-evaluation triggers
• Batch layout generation (diagrams, summaries)
• Federation integrity checks

Automation Roadmap

Automation increases with maturity.

POC (Low Automation)

Automated

• Claim normalization
• Light scenario templates
• Evidence metadata extraction
• Simple verdict drafts (internal only)

Human

• All scenario definitions
• Evidence interpretation
• Verdict creation
• Governance

Beta 0 (Medium Automation)

Automated

• Detailed scenario drafts
• Evidence reliability scoring
• Cross-scenario comparisons
• Contradiction detection (local + remote nodes)
• Internal Truth Landscape drafts

Human

• Scenario approval
• Final verdict validation

Release 1.0 (High Automation)

Automated

• Full scenario generation (definitions, assumptions, boundaries)
• Evidence relevance scoring and ranking
• Bayesian verdict scoring across scenario sets
• Multi-scenario summary generation
• Anomaly detection across nodes
• AKEL-assisted federated synchronization

Human

• Final approval of all scenarios and verdicts
• Ethical decisions
• Oversight and conflict resolution

Automation Levels

Level 0 — Human-Centric (POC)

AI is purely advisory, nothing auto-published.

Level 1 — Assisted (Beta 0)

AI drafts structures; humans approve each part.

Level 2 — Structured (Release 1.0)

AI produces near-complete drafts; humans refine.

Level 3 — Distributed Intelligence (Future)

Nodes exchange embeddings, contradiction alerts, and scenario templates.
Humans still approve everything.

Automation Matrix

Always Human

• Final verdict approval  
• Scenario validity  
• Ethical decisions  
• Dispute resolution  

Mostly AI (Human Validation Needed)

• Claim normalization  
• Clustering  
• Evidence metadata  
• Reliability heuristics  
• Scenario drafts  
• Contradiction detection  

Mixed

• Definitions of ambiguous terms  
• Boundary choices  
• Assumption evaluation  
• Evidence selection  
• Verdict reasoning  

Diagram References

Automation Roadmap
This diagram shows the automation roadmap from POC through Release 1.0.

Automation Roadmap Mermaid

graph LR
 subgraph "Quality Assurance Evolution"
 QA1["Initial: High Sampling
Higher rates for Tier A
Moderate rates for Tier B
Lower rates for Tier C"]
 QA2["Intermediate: Strategic Sampling
Focus on high-value learning
Sample new domains more
Reduce routine sampling"]
 QA3["Mature: Anomaly-Triggered
Sample based on metrics
Investigate unusual patterns
Strategic domain sampling"]
 
 QA1 --> QA2
 QA2 --> QA3
 end
 
 subgraph "POC: Proof of Concept"
 POC["POC Features
- Tier C Only
- Basic AKEL Processing
- Simple Risk Classification
- High Audit Sampling"]
 end
 
 subgraph "Release 0.5: Limited Production"
 R05["R0.5 Features
- Tier A/B/C Published
- All auto-publication
- Risk Labels Active
- Contradiction Detection
- Sampling-Based QA"]
 end
 
 subgraph "Release 1.0: Full Production"
 R10["R1.0 Features
- Comprehensive AI Publication
- Strategic Audits Only
- Federated Nodes (Beta)
- Cross-Node Data Sharing
- Mature Algorithm Performance"]
 end
 
 subgraph "Future: Distributed Intelligence"
 Future["Future Features
- Advanced Pattern Detection
- Global Contradiction Network
- Minimal Human QA (Anomalies Only)
- Full Federation"]
 end
 
 POC --> R05
 R05 --> R10
 R10 --> Future
 
 style POC fill:#e1f5ff
 style R05 fill:#d4edff
 style R10 fill:#c7e5ff
 style Future fill:#baddff

Automation Philosophy: At all stages, AKEL publishes automatically. Humans improve algorithms, not review content.

Sampling Rates: Start higher for learning, reduce as confidence grows. Rates are recommendations, not commitments.

Manual vs Automated matrix

Manual vs Automated matrix Mermaid

graph TD
 subgraph "Automated by AKEL"
 A1["Claim Evaluation
- Evidence extraction
- Source scoring
- Verdict generation
- Risk classification
- Publication"]
 A2["Quality Assessment
- Contradiction detection
- Confidence scoring
- Pattern recognition
- Anomaly flagging"]
 A3["Content Management
- Scenario generation
- Evidence linking
- Source tracking
- Version control"]
 end
 subgraph "Human Responsibilities"
 H1["Algorithm Improvement
- Monitor performance metrics
- Identify systematic issues
- Propose fixes
- Test improvements
- Deploy updates"]
 H2["Policy Governance
- Set evaluation criteria
- Define risk tiers
- Establish thresholds
- Update guidelines"]
 H3["Exception Handling
- Review AKEL-flagged items
- Handle abuse/manipulation
- Address safety concerns
- Manage legal issues"]
 H4["Strategic Decisions
- Budget and resources
- Hiring and roles
- Major policy changes
- Partnership agreements"]
 end
 style A1 fill:#c7e5ff
 style A2 fill:#c7e5ff
 style A3 fill:#c7e5ff
 style H1 fill:#ffe5cc
 style H2 fill:#ffe5cc
 style H3 fill:#ffe5cc
 style H4 fill:#ffe5cc

Key Principle: AKEL handles all content decisions. Humans improve the system, not the data.
Never Manual:
- Individual claim approval
- Routine content review
- Verdict overrides (fix algorithm instead)
- Publication gates