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

graph LR
    subgraph QA[Quality Assurance Evolution]
        QA1[Initial High Sampling]
        QA2[Intermediate Strategic]
        QA3[Mature Anomaly-Triggered]

        QA1 --> QA2
        QA2 --> QA3
    end

    subgraph POC[POC CURRENT]
        POC_F[POC Features]
    end

    subgraph R05[Release 0.5]
        R05_F[Limited Production]
    end

    subgraph R10[Release 1.0]
        R10_F[Full Production]
    end

    subgraph Future[Future]
        Future_F[Distributed Intelligence]
    end

    POC_F --> R05_F
    R05_F --> R10_F
    R10_F --> Future_F

Phase Details

POC (Current v2.6.33)

• All content analyzed
• Basic AKEL Processing
• No risk tiers yet
• No sampling audits

Release 0.5 (Planned)

• Tier A/B/C Published
• All auto-publication
• Risk Labels Active
• Contradiction Detection
• Sampling-Based QA

Release 1.0 (Planned)

• Comprehensive AI Publication
• Strategic Audits Only
• Federated Nodes Beta
• Cross-Node Data Sharing
• Mature Algorithm Performance

Future (V2.0+)

• Advanced Pattern Detection
• Global Contradiction Network
• Minimal Human QA
• Full Federation

Philosophy

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

Sampling Rates: Start higher for learning, reduce as confidence grows.

Automation Maturity Progression

graph TD
    POC[Level 0 POC Demo CURRENT]
    R05[Level 0.5 Limited Production]
    R10[Level 1.0 Full Production]
    R20[Level 2.0+ Distributed Intelligence]

    POC --> R05
    R05 --> R10
    R10 --> R20

Level Descriptions

Current Implementation (v2.6.33)

Key Principles

Across All Levels:

• AKEL makes all publication decisions
• No human approval gates
• Humans monitor metrics and improve algorithms
• Risk tiers guide audit priorities, not publication
• Sampling audits inform improvements

Manual vs Automated Matrix

graph TD
    subgraph Automated[Automated by AKEL]
        A1[Claim Evaluation]
        A2[Quality Assessment]
        A3[Content Management]
    end
    subgraph Human[Human Responsibilities]
        H1[Algorithm Improvement]
        H2[Policy Governance]
        H3[Exception Handling]
        H4[Strategic Decisions]
    end

Automated by AKEL

Human Responsibilities

Key Principles

Never Manual:

• Individual claim approval
• Routine content review
• Verdict overrides (fix algorithm instead)
• Publication gates

Key Principle: AKEL handles all content decisions. Humans improve the system, not the data.