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Transparency and Explainability — Guardrails and Fix Pattern
This page defines the structural requirements for AI systems to remain auditable, interpretable, and transparent.
Without explainability, users and regulators cannot trust that outputs are valid — even if accuracy is high.
When to use this page
- Stakeholders demand reproducible reasoning paths.
- Clients or regulators ask “why did the model output this?”
- Users complain that citations are missing or wrong.
- Debug sessions reveal black-box decisions without anchors.
Acceptance targets
- Each output includes cite-then-explain schema.
- ΔS(question, retrieved) ≤ 0.45 and convergent across three paraphrases.
- λ_observe stable across reruns with identical inputs.
- Explanations trace back to snippets with offsets, tokens, and section IDs.
- Logs capture ΔS, λ, E_resonance, and citations for every answer.
Common failures → exact fixes
| Symptom | Likely cause | Open this |
|---|---|---|
| Answers lack citations | missing data contract enforcement | data-contracts.md, retrieval-traceability.md |
| Explanations differ across runs | λ instability | context-drift.md, entropy-collapse.md |
| Outputs hide retrieval anchors | schema drift in pipeline | retrieval-playbook.md |
| Black-box API decisions | provider hides logs | LLM Providers README |
| Non-reproducible outputs | no evaluation harness | eval_playbook.md |
Fix in 60 seconds
-
Cite-first enforcement
Every answer must show citations before reasoning. -
Traceability schema
Log snippet_id, section_id, source_url, offsets, and tokens. -
ΔS + λ probes
Run three paraphrase tests. If λ flips, lock schema with BBAM clamp. -
Explainability prompt
Require explicit reasoning trace. Forbid free text without anchors. -
Audit trail
Store ΔS, λ, E_resonance, and retrieval anchors per request.
Minimal checklist for explainability
- All answers use cite-then-explain.
- Traceability schema enforced across pipeline.
- ΔS and λ logged and monitored.
- Outputs reproducible across three paraphrases.
- Explainability policy published and versioned.
🔗 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 |
🧭 Explore More
| Module | Description | Link |
|---|---|---|
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | View → |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | View → |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | View → |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | View → |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | View → |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | View → |
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | Start → |
👑 Early Stargazers: See the Hall of Fame —
Engineers, hackers, and open source builders who supported WFGY from day one.
⭐ WFGY Engine 2.0 is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board.
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