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
    3. 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.
    4. 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
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.

Automation Level
This diagram shows the progression of automation levels from POC through Release 1.0 and beyond.

Automation Level Mermaid

graph TD
 subgraph "Automation Maturity Progression"
 POC["Level 0: POC/Demo
- Tier C only
- AKEL generates, publishes with disclaimers
- High sampling audit
- Proof of concept"]
 
 R05["Release 0.5: Limited Production
- Tier B/C auto-published
- Tier A flagged for moderator review
- Higher sampling initially
- Algorithm improvement focus"]
 
 R10["Release 1.0: Full Production
- All tiers auto-published
- Clear risk labels on all content
- Reduced sampling as confidence grows
- Mature algorithm performance"]
 
 R20["Release 2.0+: Distributed Intelligence
- Federated multi-node operation
- Cross-node audit sharing
- Advanced pattern detection
- Strategic sampling only"]
 
 POC --> R05
 R05 --> R10
 R10 --> R20
 end
 
 style POC fill:#e1f5ff
 style R05 fill:#d4edff
 style R10 fill:#c7e5ff
 style R20 fill:#baddff

Key Principles Across All Levels:
- AKEL makes all publication decisions
- No human approval gates at any level
- Humans monitor metrics and improve algorithms
- Sampling audits inform improvements, don't block publication
- Risk tiers guide audit priorities, not publication permissions

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