6.6 KiB
Ethics and Bias Mitigation — Guardrails and Fix Pattern
This page defines the structural repairs required to keep AI systems ethically safe, bias-aware, and aligned with human values.
Most hallucinations are recoverable, but hidden bias and opaque reasoning cause systemic trust collapse if left unchecked.
When to use this page
- Model outputs differ systematically across demographic groups.
- Stakeholders require fairness and accountability reports.
- Ethics board or client requests bias audits.
- Outputs lack reproducibility and reasoning transparency.
Acceptance targets
- Bias probes across at least 3 demographic splits show ΔS ≤ 0.45 variance.
- λ remains convergent across all fairness probes.
- Each generated answer includes citation-first evidence.
- Ethics log captures question, snippet, ΔS, λ, and bias probe result.
- Corrective loop in place for flagged cases.
Common failures → exact fixes
| Symptom | Likely cause | Open this |
|---|---|---|
| Model amplifies stereotypes | no fairness probes | eval_playbook.md |
| Minority queries return lower recall | chunk or metric skew | embedding-vs-semantic.md, vectorstore_fragmentation.md |
| Outputs differ between identical paraphrases | λ instability | context-drift.md, entropy-collapse.md |
| Reasoning path hidden | missing explainability schema | retrieval-traceability.md, data-contracts.md |
| No escalation route for ethics issues | absent governance policy | policy_baseline.md |
Fix in 60 seconds
-
Bias probes
Run three-paraphrase tests across gender, language, or region. -
ΔS / λ monitoring
If ΔS variance ≥ 0.60 or λ diverges, trigger mitigation. -
Explainability enforced
Require cite-then-explain schema. No free text reasoning. -
Corrective loop
Add human-in-the-loop or reweight embedding index. -
Escalation
Ethics board or compliance log receives flagged cases.
Minimal bias mitigation checklist
- Weekly fairness probes logged with ΔS and λ.
- Outputs audited across ≥3 demographic splits.
- Each citation-first answer tied to provenance schema.
- Ethics incidents escalated within 24h.
- Bias mitigation 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.
