Frequently Asked Questions (FAQ)

Common questions about FactHarbor's design, functionality, and approach.

1. How do claims get evaluated in FactHarbor?

1.1 User Submission

Who: Anyone can submit claims
Process: User submits claim text + source URLs
Speed: Typically <20 seconds to verdict

1.2 AKEL Processing (Automated)

What: AI Knowledge Extraction Layer analyzes claim
Steps:

• Parse claim into testable components
• Extract evidence from provided sources
• Score source credibility
• Generate verdict with confidence level
• Assign risk tier
• Publish automatically
  Authority: AKEL makes all content decisions
  Scale: Can process millions of claims

1.3 Continuous Improvement (Human Role)

What: Humans improve the system, not individual verdicts
Activities:

• Monitor aggregate performance metrics
• Identify systematic errors
• Propose algorithm improvements
• Update policies and rules
• Test changes before deployment
  NOT: Reviewing individual claims for approval
  Focus: Fix the system, not the data

1.4 Exception Handling

When AKEL flags for review:

• Low confidence verdict
• Detected manipulation attempt
• Unusual pattern requiring attention
  Moderator role:
• Reviews flagged items
• Takes action on abuse/manipulation
• Proposes detection improvements
• Does NOT override verdicts

1.5 Why This Model Works

Scale: Automation handles volume humans cannot
Consistency: Same rules applied uniformly
Transparency: Algorithms can be audited
Improvement: Systematic fixes benefit all claims

2. What prevents FactHarbor from becoming another echo chamber?

FactHarbor includes multiple safeguards against echo chambers and filter bubbles:
Mandatory Contradiction Search:

• AI must actively search for counter-evidence, not just confirmations
• System checks for echo chamber patterns in source clusters
• Flags tribal or ideological source clustering
• Requires diverse perspectives across political/ideological spectrum
  Multiple Scenarios:
• Claims are evaluated under different interpretations
• Reveals how assumptions change conclusions
• Makes disagreements understandable, not divisive
  Transparent Reasoning:
• All assumptions, definitions, and boundaries are explicit
• Evidence chains are traceable
• Uncertainty is quantified, not hidden
  Audit System:
• Human auditors check for bubble patterns
• Feedback loop improves AI search diversity
• Community can flag missing perspectives
  Federation:
• Multiple independent nodes with different perspectives
• No single entity controls "the truth"
• Cross-node contradiction detection

3. How does FactHarbor handle claims that are "true in one context but false in another"?

This is exactly what FactHarbor is designed for:
Scenarios capture contexts:

• Each scenario defines specific boundaries, definitions, and assumptions
• The same claim can have different verdicts in different scenarios
• Example: "Coffee is healthy" depends on:
  • Definition of "healthy" (reduces disease risk? improves mood? affects specific conditions?)
  • Population (adults? pregnant women? people with heart conditions?)
  • Consumption level (1 cup/day? 5 cups/day?)
  • Time horizon (short-term? long-term?)
    Truth Landscape:
• Shows all scenarios and their verdicts side-by-side
• Users see *why* interpretations differ
• No forced consensus when legitimate disagreement exists
  Explicit Assumptions:
• Every scenario states its assumptions clearly
• Users can compare how changing assumptions changes conclusions
• Makes context-dependence visible, not hidden

4. What makes FactHarbor different from traditional fact-checking sites?

Traditional Fact-Checking:

• Binary verdicts: True / Mostly True / False
• Single interpretation chosen by fact-checker
• Often hides legitimate contextual differences
• Limited ability to show *why* people disagree
  FactHarbor:
• Multi-scenario: Shows multiple valid interpretations
• Likelihood-based: Ranges with uncertainty, not binary labels
• Transparent assumptions: Makes boundaries and definitions explicit
• Version history: Shows how understanding evolves
• Contradiction search: Actively seeks opposing evidence
• Federated: No single authority controls truth

5. How do you prevent manipulation or coordinated misinformation campaigns?

Quality Gates:

• Automated checks before AI-generated content publishes
• Source quality verification
• Mandatory contradiction search
• Bubble detection for coordinated campaigns
  Audit System:
• Stratified sampling catches manipulation patterns
• Trusted Contributor auditors validate AI research quality
• Failed audits trigger immediate review
  Transparency:
• All reasoning chains are visible
• Evidence sources are traceable
• AKEL involvement clearly labeled
• Version history preserved
  Moderation:
• Moderators handle abuse, spam, coordinated manipulation
• Content can be flagged by community
• Audit trail maintained even if content hidden
  Federation:
• Multiple nodes with independent governance
• No single point of control
• Cross-node contradiction detection
• Trust model prevents malicious node influence

6. What happens when new evidence contradicts an existing verdict?

FactHarbor is designed for evolving knowledge:
Automatic Re-evaluation:

1. New evidence arrives
   2. System detects affected scenarios and verdicts
   3. AKEL proposes updated verdicts
   4. Contributors/experts validate
   5. New verdict version published
   6. Old versions remain accessible
   Version History:

• Every verdict has complete history
• Users can see "as of date X, what did we know?"
• Timeline shows how understanding evolved
  Transparent Updates:
• Reason for re-evaluation documented
• New evidence clearly linked
• Changes explained, not hidden
  User Notifications:
• Users following claims are notified of updates
• Can compare old vs new verdicts
• Can see which evidence changed conclusions

7. Who can submit claims to FactHarbor?

Anyone - even without login:
Readers (no login required):

• Browse and search all published content
• Submit text for analysis
• New claims added automatically unless duplicates exist
• System deduplicates and normalizes
  Contributors (logged in):
• Everything Readers can do
• Submit evidence sources
• Suggest scenarios
• Participate in discussions
  Workflow:

1. User submits text (as Reader or Contributor)
   2. AKEL extracts claims
   3. Checks for existing duplicates
   4. Normalizes claim text
   5. Assigns risk tier
   6. Generates scenarios (draft)
   7. Runs quality gates
   8. Publishes as AI-Generated (Mode 2) if passes

8. What are "risk tiers" and why do they matter?

Risk tiers determine review requirements and publication workflow:
Tier A (High Risk):

• Domains: Medical, legal, elections, safety, security, major financial
• Publication: AI can publish with warnings, expert review required for "AKEL-Generated" status
• Audit rate: Recommendation 30-50%
• Why: Potential for significant harm if wrong
  Tier B (Medium Risk):
• Domains: Complex policy, science causality, contested issues
• Publication: AI can publish immediately with clear labeling
• Audit rate: Recommendation 10-20%
• Why: Nuanced but lower immediate harm risk
  Tier C (Low Risk):
• Domains: Definitions, established facts, historical data
• Publication: AI publication default
• Audit rate: Recommendation 5-10%
• Why: Well-established, low controversy
  Assignment:
• AKEL suggests tier based on domain, keywords, impact
• Moderators and Trusted Contributors can override
• Risk tiers reviewed based on audit outcomes

9. How does federation work and why is it important?

Federation Model:

• Multiple independent FactHarbor nodes
• Each node has own database, AKEL, governance
• Nodes exchange claims, scenarios, evidence, verdicts
• No central authority
  Why Federation Matters:
• Resilience: No single point of failure or censorship
• Autonomy: Communities govern themselves
• Scalability: Add nodes to handle more users
• Specialization: Domain-focused nodes (health, energy, etc.)
• Trust diversity: Multiple perspectives, not single truth source
  How Nodes Exchange Data:

1. Local node creates versions
   2. Builds signed bundle
   3. Pushes to trusted neighbor nodes
   4. Remote nodes validate signatures and lineage
   5. Accept or branch versions
   6. Local re-evaluation if needed
   Trust Model:

• Trusted nodes → auto-import
• Neutral nodes → import with review
• Untrusted nodes → manual only

10. Can experts disagree in FactHarbor?

Yes - and that's a feature, not a bug:
Multiple Scenarios:

• Trusted Contributors can create different scenarios with different assumptions
• Each scenario gets its own verdict
• Users see *why* experts disagree (different definitions, boundaries, evidence weighting)
  Parallel Verdicts:
• Same scenario, different expert interpretations
• Both verdicts visible with expert attribution
• No forced consensus
  Transparency:
• Trusted Contributor reasoning documented
• Assumptions stated explicitly
• Evidence chains traceable
• Users can evaluate competing expert opinions
  Federation:
• Different nodes can have different expert conclusions
• Cross-node branching allowed
• Users can see how conclusions vary across nodes

11. What prevents AI from hallucinating or making up facts?

Multiple Safeguards:
Quality Gate 4: Structural Integrity:

• Fact-checking against sources
• No hallucinations allowed
• Logic chain must be valid and traceable
• References must be accessible and verifiable
  Evidence Requirements:
• Primary sources required
• Citations must be complete
• Sources must be accessible
• Reliability scored
  Audit System:
• Human auditors check AI-generated content
• Hallucinations caught and fed back into training
• Patterns of errors trigger system improvements
  Transparency:
• All reasoning chains visible
• Sources linked
• Users can verify claims against sources
• AKEL outputs clearly labeled
  Human Oversight:
• Tier A marked as highest risk
• Audit sampling catches errors
• Community can flag issues

12. How does FactHarbor make money / is it sustainable?

[ToDo: Business model and sustainability to be defined]
Potential models under consideration:

• Non-profit foundation with grants and donations
• Institutional subscriptions (universities, research organizations, media)
• API access for third-party integrations
• Premium features for power users
• Federated node hosting services
  Core principle: Public benefit mission takes priority over profit.

13. Related Pages

• Requirements (Roles)
• AKEL (AI Knowledge Extraction Layer)
• Automation
• Federation & Decentralization
• Mission & Purpose

20. Glossary / Key Terms

Phase 0 vs POC v1

These terms refer to the same stage of FactHarbor's development:

• Phase 0 - Organisational perspective: Pre-alpha stage with founder-led governance
• POC v1 - Technical perspective: Proof of Concept demonstrating AI-generated publication
  Both describe the current development stage where the platform is being built and initially validated.

Beta 0

The next development stage after POC, featuring:

• External testers
• Basic federation experiments
• Enhanced automation

Release 1.0

The first public release featuring:

• Full federation support
• 2000+ concurrent users
• Production-grade infrastructure