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

POC v1 (Fully Automated "Text to Truth Landscape")

The goal of POC v1 is to validate the automated reasoning capabilities of the data model without human intervention.

Workflow

1. Input: User pastes a block of raw text.
2. Deep Analysis (Background): The system autonomously performs the full pipeline before displaying the text:

• Extraction & Normalisation
• Scenario & Sub-query generation
• Evidence retrieval & Verdict computation

1. Visualisation (Extraction & Marking): The system displays the text with claims extracted and marked.

• Verdict-Based Coloring: The extraction highlights (e.g. Orange/Green) are chosen according to the computed verdict for each claim.

1. Inspection: User clicks a highlighted claim to see the Reasoning Trail, showing exactly which evidence and sub-queries led to that verdict.

Technical Scope

• Fully Automated: No human-in-the-loop for this phase.
• Structured Sub-Queries: Logic is generated by decomposing claims into the FactHarbor data model.
• Latency: Focus on accuracy of reasoning over real-time speed for v1.

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, Metadata extraction, Internal drafts.
• 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.
• Human: Scenario approval, Final verdict validation.

Release 1.0 (High Automation)

• Automated: Full scenario generation, Evidence relevance ranking, Bayesian verdict scoring, Anomaly detection, Federation sync.
• Human: Final approval, Ethical decisions, Oversight.

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 and alerts; humans still approve.

Automation Matrix

• Always Human: Final verdict, Scenario validity, Ethics, Disputes.
• Mostly AI: Normalization, Clustering, Metadata, Heuristics, Alerts.
• Mixed: Definitions, Boundaries, Assumptions, 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.