Workflows

This page describes the core workflows for content creation, review, and publication in FactHarbor.

Overview

FactHarbor workflows support three publication modes with risk-based review:

• Mode 1 (Draft): Internal only, failed quality gates or pending review
• Mode 2 (AI-Generated): Public with AI-generated label, passed quality gates
• Mode 3 (Human-Reviewed): Public with human-reviewed status, highest trust

Workflows vary by Risk Tier (A/B/C) and Content Type (Claim, Scenario, Evidence, Verdict).

Claim Submission & Publication Workflow

Step 1: Claim Submission

Actor: Contributor or AKEL

Actions:

• Submit claim text
• Provide initial sources (optional for human contributors, mandatory for AKEL)
• System assigns initial AuthorType (Human or AI)

Output: Claim draft created

Step 2: AKEL Processing

Automated Steps:

1. Claim extraction and normalization
   2. Classification (domain, type, evaluability)
   3. Risk tier assignment (A/B/C suggested)
   4. Initial scenario generation
   5. Evidence search
   6. Contradiction search (mandatory)
   7. Quality gate validation

Output: Processed claim with risk tier and quality gate results

Step 3: Quality Gate Checkpoint

Gates Evaluated:

• Source quality
• Contradiction search completion
• Uncertainty quantification
• Structural integrity

Outcomes:

• All gates pass → Proceed to Mode 2 publication (if Tier B or C)
• Any gate fails → Mode 1 (Draft), flag for human review
• Tier A → Mode 2 with warnings + auto-escalate to expert queue

Step 4: Publication (Risk-Tier Dependent)

Tier C (Low Risk):

• Direct to Mode 2: AI-generated, public, clearly labeled
• User can request human review
• Sampling audit applies

Tier B (Medium Risk):

• Direct to Mode 2: AI-generated, public, clearly labeled
• Higher audit sampling rate
• High-engagement content may auto-escalate

Tier A (High Risk):

• Mode 2 with warnings: AI-generated, public, prominent disclaimers
• Auto-escalated to expert review queue
• User warnings displayed
• Highest audit sampling rate

Step 5: Human Review (Optional for B/C, Escalated for A)

Triggers:

• User requests review
• Audit flags issues
• High engagement (Tier B)
• Automatic (Tier A)

Process:

1. Reviewer/Expert examines claim
   2. Validates quality gates
   3. Checks contradiction search results
   4. Assesses risk tier appropriateness
   5. Decision: Approve, Request Changes, or Reject

Outcomes:

• Approved → Mode 3 (Human-Reviewed)
• Changes Requested → Back to contributor or AKEL for revision
• Rejected → Rejected status with reasoning

Scenario Creation Workflow

Step 1: Scenario Generation

Automated (AKEL):

• Generate scenarios for claim
• Define boundaries, assumptions, context
• Identify evaluation methods

Manual (Expert/Reviewer):

• Create custom scenarios
• Refine AKEL-generated scenarios
• Add domain-specific nuances

Step 2: Scenario Validation

Quality Checks:

• Completeness (definitions, boundaries, assumptions clear)
• Relevance to claim
• Evaluability
• No circular logic

Risk Tier Assignment:

• Inherits from parent claim
• Can be overridden by expert if scenario increases/decreases risk

Step 3: Scenario Publication

Mode 2 (AI-Generated):

• Tier B/C scenarios can publish immediately
• Subject to sampling audits

Mode 1 (Draft):

• Tier A scenarios default to draft
• Require expert validation for Mode 2 or Mode 3

Evidence Evaluation Workflow

Step 1: Evidence Search & Retrieval

AKEL Actions:

• Search academic databases, reputable media
• Mandatory contradiction search (counter-evidence, reservations)
• Extract metadata (author, date, publication, methodology)
• Assess source reliability

Quality Requirements:

• Primary sources preferred
• Diverse perspectives included
• Echo chambers flagged
• Conflicting evidence acknowledged

Step 2: Evidence Summarization

AKEL Generates:

• Summary of evidence
• Relevance assessment
• Reliability score
• Limitations and caveats
• Conflicting evidence summary

Quality Gate: Structural integrity, source quality

Step 3: Evidence Review

Reviewer/Expert Validates:

• Accuracy of summaries
• Appropriateness of sources
• Completeness of contradiction search
• Reliability assessments

Outcomes:

• Mode 2: Evidence summaries published as AI-generated
• Mode 3: After human validation
• Mode 1: Failed quality checks or pending expert review

Verdict Generation Workflow

Step 1: Verdict Computation

AKEL Computes:

• Verdict across scenarios
• Confidence scores
• Uncertainty quantification
• Key assumptions
• Limitations

Inputs:

• Claim text
• Scenario definitions
• Evidence assessments
• Contradiction search results

Step 2: Verdict Validation

Quality Gates:

• All four gates apply (source, contradiction, uncertainty, structure)
• Reasoning chain must be traceable
• Assumptions must be explicit

Risk Tier Check:

• Tier A: Always requires expert validation for Mode 3
• Tier B: Mode 2 allowed, audit sampling
• Tier C: Mode 2 default

Step 3: Verdict Publication

Mode 2 (AI-Generated Verdict):

• Clear labeling with confidence scores
• Uncertainty disclosure
• Links to reasoning trail
• User can request expert review

Mode 3 (Expert-Validated Verdict):

• Human reviewer/expert stamp
• Additional commentary (optional)
• Highest trust level

Audit Workflow

Step 1: Audit Sampling Selection

Stratified Sampling:

• Risk tier priority (A > B > C)
• Low confidence scores
• High traffic content
• Novel topics
• User flags

Sampling Rates (Recommendations):

• Tier A: 30-50%
• Tier B: 10-20%
• Tier C: 5-10%

Step 2: Audit Execution

Auditor Actions:

1. Review sampled AI-generated content
   2. Validate quality gates were properly applied
   3. Check contradiction search completeness
   4. Assess reasoning quality
   5. Identify errors or hallucinations

Audit Outcome:

• Pass: Content remains in Mode 2, logged as validated
• Fail: Content flagged for review, system improvement triggered

Step 3: Feedback Loop

System Improvements:

• Failed audits analyzed for patterns
• AKEL parameters adjusted
• Quality gates refined
• Risk tier assignments recalibrated

Transparency:

• Audit statistics published periodically
• Patterns shared with community
• System improvements documented

Mode Transition Workflow

Mode 1 → Mode 2

Requirements:

• All quality gates pass
• Risk tier B or C (or A with warnings)
• Contradiction search completed

Trigger: Automatic upon quality gate validation

Mode 2 → Mode 3

Requirements:

• Human reviewer/expert validation
• Quality standards confirmed
• For Tier A: Expert approval required
• For Tier B/C: Reviewer approval sufficient

Trigger: Human review completion

Mode 3 → Mode 1 (Demotion)

Rare - Only if:

• New evidence contradicts verdict
• Error discovered in reasoning
• Source retraction

Process:

1. Content flagged for re-evaluation
   2. Moved to draft (Mode 1)
   3. Re-processed through workflow
   4. Reason for demotion documented

User Actions Across Modes

On Mode 1 (Draft) Content

Contributors:

• Edit their own drafts
• Submit for review

Reviewers/Experts:

• View and comment
• Request changes
• Approve for Mode 2 or Mode 3

On Mode 2 (AI-Generated) Content

All Users:

• Read and use content
• Request human review
• Flag for expert attention
• Provide feedback

Reviewers/Experts:

• Validate for Mode 3 transition
• Edit and refine
• Adjust risk tier if needed

On Mode 3 (Human-Reviewed) Content

All Users:

• Read with highest confidence
• Still can flag if new evidence emerges

Reviewers/Experts:

• Update if needed
• Trigger re-evaluation if new evidence

Diagram References

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.

Related Pages

• AKEL (AI Knowledge Extraction Layer)
• Automation
• Requirements (Roles)
• Governance