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Version 1.5 by Robert Schaub on 2026/01/20 20:30
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1 = AKEL – AI Knowledge Extraction Layer =
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3 **Version:** 0.9.70 **Last Updated:** December 21, 2025 **Status:** CORRECTED - Automation Philosophy Consistent AKEL is FactHarbor's automated intelligence subsystem. Its purpose is to reduce human workload, enhance consistency, and enable scalable knowledge processing. AKEL outputs are marked with **AuthorType = AI** and published according to risk-based policies (see Publication Modes below). AKEL operates in two modes:
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5 * **Single-node mode** (POC & Beta 0)
6 * **Federated multi-node mode** (Release 1.0+) == 1. Core Philosophy: Automation First == **V0.9.50+ Philosophy Shift:** FactHarbor uses **"Improve the system, not the data"** approach: * ✅ **Automated Publication:** AI-generated content publishes immediately after passing quality gates
7 * ✅ **Quality Gates:** Automated checks (not human approval)
8 * ✅ **Sampling Audits:** Humans analyze patterns for system improvement (not individual approval)
9 * ❌ **NO approval workflows:** No review queues, no moderator gatekeeping for content quality
10 * ❌ **NO manual fixes:** If output is wrong, improve the algorithm/prompts **Why This Matters:** Traditional approach: Human reviews every output → Bottleneck, inconsistent FactHarbor approach: Automated quality gates + pattern-based improvement → Scalable, consistent == 2. Publication Modes == **V0.9.70 CLARIFICATION:** FactHarbor uses **TWO publication modes** (not three): === Mode 1: Draft-Only === **Status:** Not visible to public **When Used:**
11 * Quality gates failed
12 * Confidence below threshold
13 * Structural integrity issues
14 * Insufficient evidence **What Happens:**
15 * Content remains private
16 * System logs failure reasons
17 * Prompts/algorithms improved based on patterns
18 * Content may be re-processed after improvements **NOT "pending human approval"** - it's blocked because it doesn't meet automated quality standards. === Mode 2: AI-Generated (Public) === **Status:** Published and visible to all users **When Used:**
19 * Quality gates passed
20 * Confidence ≥ threshold
21 * Meets structural requirements
22 * Sufficient evidence found **Includes:**
23 * Confidence score displayed (0-100%)
24 * Risk tier badge (A/B/C)
25 * Quality indicators
26 * Clear "AI-Generated" labeling
27 * Sampling audit status **Labels by Risk Tier:**
28 * **Tier A (High Risk):** "⚠️ AI-Generated - High Impact Topic - Seek Professional Advice"
29 * **Tier B (Medium Risk):** "🤖 AI-Generated - May Contain Errors"
30 * **Tier C (Low Risk):** "🤖 AI-Generated" === REMOVED: "Mode 3: Human-Reviewed" === **V0.9.50 Decision:** No centralized approval workflow. **Rationale:**
31 * Defeats automation purpose
32 * Creates bottleneck
33 * Inconsistent quality
34 * Not scalable **What Replaced It:**
35 * Better quality gates
36 * Sampling audits for system improvement
37 * Transparent confidence scoring
38 * Risk-based warnings == 3. Risk Tiers (A/B/C) == Risk classification determines WARNING LABELS and AUDIT FREQUENCY, NOT approval requirements. === Tier A: High-Stakes Claims === **Examples:** Medical advice, legal interpretations, financial recommendations, safety information **Impact:**
39 * ✅ Publish immediately (if passes gates)
40 * ✅ Prominent warning labels
41 * ✅ Higher sampling audit frequency (50% audited)
42 * ✅ Explicit disclaimers ("Seek professional advice")
43 * ❌ NOT held for moderator approval **Philosophy:** Publish with strong warnings, monitor closely === Tier B: Moderate-Stakes Claims === **Examples:** Political claims, controversial topics, scientific debates **Impact:**
44 * ✅ Publish immediately (if passes gates)
45 * ✅ Standard warning labels
46 * ✅ Medium sampling audit frequency (20% audited)
47 * ❌ NOT held for moderator approval === Tier C: Low-Stakes Claims === **Examples:** Entertainment facts, sports statistics, general knowledge **Impact:**
48 * ✅ Publish immediately (if passes gates)
49 * ✅ Minimal warning labels
50 * ✅ Low sampling audit frequency (5% audited) == 4. Quality Gates (Automated, Not Human) == All AI-generated content must pass these **AUTOMATED checks** before publication: === Gate 1: Source Quality === **Automated Checks:**
51 * Primary sources identified and accessible
52 * Source reliability scored against database
53 * Citation completeness verified
54 * Publication dates checked
55 * Author credentials validated (where applicable) **If Failed:** Block publication, log pattern, improve source detection === Gate 2: Contradiction Search (MANDATORY) === **The system MUST actively search for:** * **Counter-evidence** – Rebuttals, conflicting results, contradictory studies
56 * **Reservations** – Caveats, limitations, boundary conditions
57 * **Alternative interpretations** – Different framings, definitions
58 * **Bubble detection** – Echo chambers, ideologically isolated sources **Search Coverage Requirements:**
59 * Academic literature (BOTH supporting AND opposing views)
60 * Diverse media across political/ideological perspectives
61 * Official contradictions (retractions, corrections, amendments)
62 * Cross-cultural and international perspectives **Search Must Avoid Algorithmic Bubbles:**
63 * Deliberately seek opposing viewpoints
64 * Check for echo chamber patterns
65 * Identify tribal source clustering
66 * Flag artificially constrained search space
67 * Verify diversity of perspectives **Outcomes:**
68 * Strong counter-evidence → Auto-escalate to Tier B or draft-only
69 * Significant uncertainty → Require uncertainty disclosure in verdict
70 * Bubble indicators → Flag for sampling audit
71 * Limited perspective diversity → Expand search or flag **If Failed:** Block publication, improve search algorithms === Gate 3: Uncertainty Quantification === **Automated Checks:**
72 * Confidence scores calculated for all claims and verdicts
73 * Limitations explicitly stated
74 * Data gaps identified and disclosed
75 * Strength of evidence assessed
76 * Alternative scenarios considered **If Failed:** Block publication, improve confidence scoring === Gate 4: Structural Integrity === **Automated Checks:**
77 * No hallucinations detected (fact-checking against sources)
78 * Logic chain valid and traceable
79 * References accessible and verifiable
80 * No circular reasoning
81 * Premises clearly stated **If Failed:** Block publication, improve hallucination detection **CRITICAL:** If any gate fails:
82 * ✅ Content remains in draft-only mode
83 * ✅ Failure reason logged
84 * ✅ Failure patterns analyzed for system improvement
85 * ❌ **NOT "sent for human review"**
86 * ❌ **NOT "manually overridden"** **Philosophy:** Fix the system that generated bad output, don't manually fix individual outputs. == 5. Sampling Audit System == **Purpose:** Improve the system through pattern analysis (NOT approve individual outputs) === 5.1 How Sampling Works === **Stratified Sampling Strategy:** Audits prioritize:
87 * **Risk tier** (Tier A: 50%, Tier B: 20%, Tier C: 5%)
88 * **AI confidence score** (low confidence → higher sampling rate)
89 * **Traffic and engagement** (high-visibility content audited more)
90 * **Novelty** (new claim types, new domains, emerging topics)
91 * **Disagreement signals** (user flags, contradiction alerts, community reports) **NOT:** Review queue for approval **IS:** Statistical sampling for quality monitoring === 5.2 Audit Process === 1. **System selects** content for audit based on sampling strategy
92 2. **Human auditor** reviews AI-generated content against quality standards
93 3. **Auditor validates or identifies issues:** * Claim extraction accuracy * Scenario appropriateness * Evidence relevance and interpretation * Verdict reasoning * Contradiction search completeness
94 4. **Audit outcome recorded** (pass/fail + detailed feedback)
95 5. **Failed audits trigger:** * Analysis of failure pattern * System improvement tasks * Algorithm/prompt adjustments
96 6. **Audit results feed back** into system improvement **CRITICAL:** Auditors analyze PATTERNS, not fix individual outputs. === 5.3 Feedback Loop (Continuous Improvement) === Audit outcomes systematically improve: * **Query templates** – Refined based on missed evidence patterns
97 * **Retrieval source weights** – Adjusted for accuracy and reliability
98 * **Contradiction detection heuristics** – Enhanced to catch missed counter-evidence
99 * **Model prompts and extraction rules** – Tuned for better claim extraction
100 * **Risk tier assignments** – Recalibrated based on error patterns
101 * **Bubble detection algorithms** – Improved to identify echo chambers **Philosophy:** "Improve the system, not the data" === 5.4 Audit Transparency === **Publicly Published:**
102 * Audit statistics (monthly)
103 * Accuracy rates by risk tier
104 * System improvements made
105 * Aggregate audit performance **Enables:**
106 * Public accountability
107 * System trust
108 * Continuous improvement visibility == 6. Human Intervention Criteria == **From Organisation.Decision-Processes:** **LEGITIMATE reasons to intervene:** * ✅ AKEL explicitly flags item for sampling audit
109 * ✅ System metrics show performance degradation
110 * ✅ Legal/safety issue requires immediate action
111 * ✅ User reports reveal systematic bias pattern **ILLEGITIMATE reasons** (system improvement needed instead): * ❌ "I disagree with this verdict" → Improve algorithm
112 * ❌ "This source should rank higher" → Improve scoring rules
113 * ❌ "Manual quality gate before publication" → Defeats purpose of automation
114 * ❌ "I know better than the algorithm" → Then improve the algorithm **Philosophy:** If you disagree with output, improve the system that generated it. == 7. Architecture Overview == === POC Architecture (POC1, POC2) === **Simple, Single-Call Approach:** ```
115 User submits article/claim ↓
116 Single AI API call ↓
117 Returns complete analysis ↓
118 Quality gates validate ↓
119 PASS → Publish (Mode 2)
120 FAIL → Block (Mode 1)
121 ``` **Components in Single Call:**
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123 1. Extract 3-5 factual claims
124 2. For each claim: verdict + confidence + risk tier + reasoning
125 3. Generate analysis summary
126 4. Generate article summary
127 5. Run basic quality checks **Processing Time:** 10-18 seconds **Advantages:** Simple, fast POC development, proves AI capability **Limitations:** No component reusability, all-or-nothing === Full System Architecture (Beta 0, Release 1.0) === **Multi-Component Pipeline:** ```
128 AKEL Orchestrator
129 ├── Claim Extractor
130 ├── Claim Classifier (with risk tier assignment)
131 ├── Scenario Generator
132 ├── Evidence Summarizer
133 ├── Contradiction Detector
134 ├── Quality Gate Validator
135 ├── Audit Sampling Scheduler
136 └── Federation Sync Adapter (Release 1.0+)
137 ``` **Processing:**
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139 * Parallel processing where possible
140 * Separate component calls
141 * Quality gates between phases
142 * Audit sampling selection
143 * Cross-node coordination (federated mode) **Processing Time:** 10-30 seconds (full pipeline) === Evolution Path === **POC1:** Single prompt → Prove concept **POC2:** Add scenario component → Test full pipeline **Beta 0:** Multi-component AKEL → Production architecture **Release 1.0:** Full AKEL + Federation → Scale == 8. AKEL and Federation == In Release 1.0+, AKEL participates in cross-node knowledge alignment: * Shares embeddings
144 * Exchanges canonicalized claim forms
145 * Exchanges scenario templates
146 * Sends + receives contradiction alerts
147 * Shares audit findings (with privacy controls)
148 * Never shares model weights
149 * Never overrides local governance Nodes may choose trust levels for AKEL-related data:
150 * Trusted nodes: auto-merge embeddings + templates
151 * Neutral nodes: require additional verification
152 * Untrusted nodes: fully manual import == 9. POC Behavior == The POC explicitly demonstrates AI-generated content publication: * ✅ Produces public AI-generated output (Mode 2)
153 * ✅ No human data sources required
154 * ✅ No human approval gate
155 * ✅ Clear "AI-Generated - POC/Demo" labeling
156 * ✅ All quality gates active (including contradiction search)
157 * ✅ Users understand this demonstrates AI reasoning capabilities
158 * ✅ Risk tier classification shown (demo purposes) **Philosophy Validation:** POC proves automation-first approach works. == 10. Related Pages == * [[Automation>>Archive.FactHarbor.Specification.Automation.WebHome]]
159 * [[Requirements (Roles)>>Archive.FactHarbor.Specification.Requirements.WebHome]]
160 * [[Workflows>>FactHarbor.Specification.Workflows.WebHome]]
161 * [[Governance>>Archive.FactHarbor.Organisation.Governance.WebHome]]
162 * [[Decision Processes>>FactHarbor.Organisation.Decision-Processes.WebHome]] **V0.9.70 CHANGES:**
163 - ❌ REMOVED: Section "Human Review Workflow (Mode 3 Publication)"
164 - ❌ REMOVED: All references to "Mode 3"
165 - ❌ REMOVED: "Human review required before publication"
166 - ✅ CLARIFIED: Two modes only (AI-Generated / Draft-Only)
167 - ✅ CLARIFIED: Quality gate failures → Block + improve system
168 - ✅ CLARIFIED: Sampling audits for improvement, NOT approval
169 - ✅ CLARIFIED: Risk tiers affect warnings/audits, NOT approval gates
170 - ✅ ENHANCED: Gate 2 (Contradiction Search) specification
171 - ✅ ADDED: Clear human intervention criteria
172 - ✅ ADDED: Detailed audit system explanation