WFGY/ProblemMap/GlobalFixMap/Governance/license_and_dataset_rights.md

License and Dataset Rights — Guardrails and Fix Pattern

🧭 Quick Return to Map

You are in a sub-page of Governance.
To reorient, go back here:

• Governance — policy enforcement and compliance controls
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

This page tracks legal and licensing risks in AI systems.
If datasets, embeddings, or outputs rely on unverified sources, you risk compliance violations, takedowns, or IP litigation.
Use this guide to enforce clear rights boundaries across ingestion, training, and generation.

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When to use this page

• You do not know the license of a dataset included in your retrieval or training pipeline.
• Mixed rights (MIT, Apache, proprietary, unknown) exist in the same corpus.
• Legal review requires proof that generated answers respect dataset terms.
• Audit asks for compliance with GDPR/CCPA or publisher licensing.

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Acceptance targets

• 100% of datasets tagged with a license identifier.
• ≥ 95% coverage of ingestion checks include license + attribution metadata.
• ΔS stability maintained when filtering licensed vs. non-licensed subsets.
• Audit logs capture license_id, rights_holder, expiry.
• LLM refuses or flags outputs when license terms are violated.

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Common failures → exact fixes

Symptom	Likely cause	Open this
Snippets from unknown or scraped sources appear	dataset missing license metadata	data-contracts.md
Open source corpus mixes with paid corpus	no role/rights separation	roles_and_access_rbac_abac.md
Model answers cite restricted publishers	no attribution enforcement	retrieval-traceability.md
Generated outputs reuse licensed patterns	prompt injection bypassing rights filter	prompt-injection.md
Training run includes expired or revoked dataset	missing expiry validation	policy_baseline.md

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Fix in 60 seconds

1. Tag every dataset
   Attach {license_id, rights_holder, expiry, attribution_required} at ingestion.

2. Separate corpora
   Open source vs. proprietary vs. paid corpora must remain in distinct retrieval indices.

3. Enforce attribution
   Require citation-first prompting for any licensed corpus.

4. Audit pipeline
   Store logs with dataset version, license, and usage context.

5. Expiry enforcement
   Block or purge expired corpora automatically.

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Minimal compliance checklist

• Every ingestion pipeline requires license metadata.
• RBAC + ABAC enforced to keep datasets separated.
• Retrieval schema logs license_id with every snippet.
• Expiry checks run nightly.
• Audit reports exportable on request.

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

Layer	Page	What it’s for
⭐ Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
⚙️ Engine	WFGY 1.0	Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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