Mission & Purpose
Mission & Purpose
Mission
FactHarbor brings clarity and transparency to a world full of unclear, controversial, and misleading information by shedding light on the context, assumptions, and evidence behind claims — empowering people to better understand and judge wisely.
Purpose
Modern society faces a deep informational crisis:
- Misinformation spreads faster than corrections
- High-quality evidence is buried under noise
- Interpretations change depending on context — but this is rarely made explicit
- Users lack tools to understand *why* information conflicts
- Claims are evaluated without clearly defined assumptions
- The concept of "truth" is increasingly politicized and weaponized
- AI accelerates both clarity and manipulation
FactHarbor exists to bring structure and transparency into this chaos.
It provides:
- A structured way to interpret claims
- Multiple valid scenarios when a claim is ambiguous
- Transparent assumptions, definitions, and boundaries
- Complete evidence provenance
- Likelihood-based verdicts rather than binary labels
- Explanations for why interpretations differ
- Neutral tools that reduce manipulation and bias
The platform is built to:
- Reveal nuance
- Expose misleading interpretations
- Eliminate ambiguity
- Help users understand how conclusions differ across valid contexts
- Support well-grounded, independent judgments
FactHarbor does not declare absolute truths.
It clarifies how thinking works, why disagreement arises, and what can be responsibly concluded.
Core Problems FactHarbor Solves
Problem 1 — Misinformation & Manipulation
Falsehoods and distortions spread rapidly through:
- Political propaganda
- Social media amplification
- Coordinated influence networks
- AI-generated fake content
Users need a structured system that resists manipulation and makes reasoning transparent.
Problem 2 — Missing Context Behind Claims
Most claims change meaning drastically depending on:
- Definitions
- Assumptions
- Boundaries
- Interpretation
FactHarbor reveals and compares these variations.
Problem 3 — "Binary Fact Checks" Fail
Most fact-checking simplifies complex claims into:
- True
- Mostly True
- False
This hides legitimate contextual differences.
FactHarbor replaces binary judgment with scenario-based, likelihood-driven evaluation.
Problem 4 — Good Evidence Is Hard to Find
High-quality evidence exists — but users often cannot:
- Locate it
- Assess its reliability
- Understand how it fits into a scenario
- Compare it with competing evidence
FactHarbor aggregates, assesses, and organizes evidence with full transparency.
Problem 5 — Claims Evolve Over Time
Research and understanding change:
- New studies emerge
- Old studies are retracted
- Consensus shifts
FactHarbor provides:
- Full entity versioning
- Verdict timelines
- Automatic re-evaluation when inputs change
Problem 6 — Users Cannot See Why People Disagree
People often assume others are ignorant or dishonest, when disagreements typically arise from:
- Different definitions
- Different implicit assumptions
- Different evidence
- Different contexts
FactHarbor exposes these underlying structures so disagreements become understandable, not divisive.
Core Concepts
Claim
A user- or AI-submitted statement whose meaning is often ambiguous and requires structured interpretation.
Key fields include:
- Text
- Type (literal, metaphorical, rhetorical, supernatural, etc.)
- Evaluability
- Safety classification
- Risk tier
- Version metadata
A claim does not receive a single verdict.
It branches into scenarios that clarify its meaning.
Scenario
A structured interpretation that clarifies what the claim means under a specific set of:
- Boundaries
- Definitions
- Assumptions
- Contextual conditions
Multiple scenarios allow claims to be understood fairly and without political or ideological bias.
Evidence
Information that supports or contradicts a scenario.
Evidence includes:
- Empirical studies
- Experimental data
- Expert consensus
- Historical records
- Contextual background
- Absence-of-evidence signals
Evidence evolves through versioning and includes reliability assessment.
Verdict
A likelihood estimate for a claim within a specific scenario based on:
- Evidence quality
- Evidence quantity
- Strength of assumptions
- Methodological reliability
- Uncertainty factors
- Comparison with competing scenarios
Each verdict is versioned and includes a historical timeline.
Summary View
A user-facing, simplified overview that:
- Highlights the most common interpretation
- Presents alternative scenarios
- Explains why interpretations differ
- Shows aggregated likelihoods
- Communicates uncertainty clearly
AI Knowledge Extraction Layer (AKEL)
The AI subsystem that:
- Interprets claims
- Proposes scenario drafts
- Retrieves evidence
- Classifies and summarizes sources
- Drafts verdicts
- Detects contradictions
- Triggers re-evaluation when inputs change
AKEL outputs follow risk-based publication model with quality gates and audit oversight.
Decentralized Federation Model
FactHarbor supports a decentralized, multi-node architecture:
- Each node stores its own claims, scenarios, and verdicts
- Nodes synchronize via a federation protocol
- Evidence may be stored locally or via IPFS
- Communities, universities, or organizations can host their own nodes
- A global, emergent consensus forms across the network without central authority
This increases resilience, autonomy, and scalability.
Vision for Impact
FactHarbor aims to:
- Reduce polarization by revealing the legitimate grounds for disagreement
- Combat misinformation by providing structured, transparent evaluation
- Empower users to make informed judgments based on evidence
- Support deliberative democracy by clarifying complex policy questions
- Enable federated knowledge so no single entity controls the truth
- Resist manipulation through transparent reasoning and quality oversight
- Evolve with research by maintaining versioned, updatable knowledge