Changes for page FAQ

Last modified by Robert Schaub on 2026/02/08 21:20

From 2.6 to 2.5

From version 2.5

edited by Robert Schaub
on 2026/01/20 20:25

Change comment: Renamed back-links.

To version 1.1

edited by Robert Schaub
on 2025/12/16 21:42

Change comment: Imported from XAR

Raw
Rendered

Summary

Page properties (2 modified, 0 added, 0 removed)

Details

Page properties

Parent

@@ -1,1 +1,1 @@
--Archive.FactHarbor 0\.9\.40.Specification.WebHome
++FactHarbor.Specification.WebHome

Content

@@ -12,7 +12,6 @@
  **What**: System dynamically researches claims using AKEL (AI Knowledge Extraction Layer)
  **Process**:
--
  * Extracts claims from submitted text
  * Generates structured sub-queries
  * Performs **mandatory contradiction search** (actively seeks counter-evidence, not just confirmations)
@@ -40,7 +40,6 @@
  **What**: Sampling audits where experts review AI-generated content
  **Rates**:
--
  * High-risk (Tier A): 30-50% sampling
  * Medium-risk (Tier B): 10-20% sampling
  * Low-risk (Tier C): 5-10% sampling
@@ -52,7 +52,6 @@
  === 1.4 Why All Three Matter ===
  **Complementary Strengths**:
--
  * **AI research**: Scale and speed for emerging claims
  * **Expert authoring**: Authority and precision for critical domains
  * **Audit feedback**: Continuous quality improvement
@@ -60,7 +60,6 @@
  **Expert Time Optimization**:
  Experts can choose where to focus their time:
--
  * Author high-priority content directly
  * Validate and edit AI-generated outputs
  * Audit samples to improve system-wide AI performance
@@ -79,7 +79,6 @@
  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
@@ -86,59 +86,53 @@
  * 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?)
++  ** 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
@@ -145,7 +145,6 @@
  * 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
@@ -153,10 +153,10 @@
  * **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
@@ -163,13 +163,11 @@
  * Bubble detection for coordinated campaigns
  **Audit System**:
--
  * Stratified sampling catches manipulation patterns
  * Expert auditors validate AI research quality
  * Failed audits trigger immediate review
  **Transparency**:
--
  * All reasoning chains are visible
  * Evidence sources are traceable
  * AKEL involvement clearly labeled
@@ -176,24 +176,22 @@
  * 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**:
--
 . New evidence arrives
 . System detects affected scenarios and verdicts
 . AKEL proposes updated verdicts
@@ -202,29 +202,26 @@
 . 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
@@ -231,7 +231,6 @@
  * System deduplicates and normalizes
  **Contributors** (logged in):
--
  * Everything Readers can do
  * Submit evidence sources
  * Suggest scenarios
@@ -238,7 +238,6 @@
  * Participate in discussions
  **Workflow**:
--
 . User submits text (as Reader or Contributor)
 . AKEL extracts claims
 . Checks for existing duplicates
@@ -248,12 +248,12 @@
 . Runs quality gates
 . 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 "Human-Reviewed" status
  * **Audit rate**: Recommendation 30-50%
@@ -260,7 +260,6 @@
  * **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%
@@ -267,7 +267,6 @@
  * **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%
@@ -274,15 +274,14 @@
  * **Why**: Well-established, low controversy
  **Assignment**:
--
  * AKEL suggests tier based on domain, keywords, impact
  * Moderators and Experts 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
@@ -289,7 +289,6 @@
  * 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
@@ -297,7 +297,6 @@
  * **Trust diversity**: Multiple perspectives, not single truth source
  **How Nodes Exchange Data**:
--
 . Local node creates versions
 . Builds signed bundle
 . Pushes to trusted neighbor nodes
@@ -306,29 +306,26 @@
 . 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**:
--
  * Experts 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**:
--
  * Expert reasoning documented
  * Assumptions stated explicitly
  * Evidence chains traceable
@@ -335,17 +335,16 @@
  * 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
@@ -352,7 +352,6 @@
  * References must be accessible and verifiable
  **Evidence Requirements**:
--
  * Primary sources required
  * Citations must be complete
  * Sources must be accessible
@@ -359,13 +359,11 @@
  * 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
@@ -372,17 +372,16 @@
  * AKEL outputs clearly labeled
  **Human Oversight**:
--
  * Tier A requires expert review for "Human-Reviewed" status
  * 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
@@ -395,34 +395,8 @@
  == 13. Related Pages ==
  * [[Requirements (Roles)>>FactHarbor.Specification.Requirements.WebHome]]
--* [[AKEL (AI Knowledge Extraction Layer)>>Archive.FactHarbor.Specification.AI Knowledge Extraction Layer (AKEL).WebHome]]
++* [[AKEL (AI Knowledge Extraction Layer)>>FactHarbor.Specification.AI Knowledge Extraction Layer (AKEL).WebHome]]
  * [[Automation>>FactHarbor.Specification.Automation.WebHome]]
  * [[Federation & Decentralization>>FactHarbor.Specification.Federation & Decentralization.WebHome]]
--* [[Mission & Purpose>>Archive.FactHarbor.Organisation.Core Problems FactHarbor Solves.WebHome]]
++* [[Mission & Purpose>>FactHarbor.Organisation.Core Problems FactHarbor Solves.WebHome]]
--== 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

Changes for page FAQ

Summary

Details

Applications

Navigation

Need help?