refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-toxicity-and-bias-mitigation
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title: Toxicity and Bias Mitigation
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [LLM Safety, Bias Mitigation, Constitutional AI, RLHF, RLAIF]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [safety, alignment, bias, rlhf, constitutional-ai]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: trl-anthropic-openai
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---
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# Toxicity and Bias Mitigation
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## 매 한 줄
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> **"매 LLM output 에서 harm, stereotype, factual bias 을 제거하면서 helpfulness 를 유지하는 alignment stack"**. 매 2017 RLHF (Christiano) → 2022 Constitutional AI (Anthropic) → 2024 deliberative alignment (OpenAI o1) → 2026 multi-stage post-training (helpfulness + harmlessness + honesty + sycophancy reduction). 매 모든 frontier model 의 production deployment 의 prerequisite.
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## 매 핵심
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### 매 taxonomy of harms
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1. **Toxicity**: hate speech, harassment, slurs.
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2. **Bias**: demographic stereotypes (gender, race, religion).
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3. **Misinformation**: false / misleading factual claims.
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4. **Manipulation**: persuasion, deception, sycophancy.
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5. **Dual-use**: bioweapon / cyber / CBRN uplift.
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6. **Privacy**: PII leakage, training data extraction.
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### 매 mitigation pipeline (modern)
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1. **Pretraining filter**: C4-style + classifiers, Common Crawl deduplication.
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2. **SFT** (supervised finetune): safe demonstrations.
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3. **RLHF / DPO** (Direct Preference Optimization 2023+): human preference.
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4. **Constitutional AI / RLAIF** (Anthropic): AI feedback against principles.
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5. **Red-teaming**: human + automated adversarial probing.
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6. **Inference-time**: classifier filters, refusal training, system prompts.
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7. **Deliberative / chain-of-thought safety** (o1, Claude 3.7+): reasoning about safety policy explicitly.
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### 매 bias measurement benchmarks
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- **BBQ** (Bias Benchmark for QA, 11 social dimensions).
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- **StereoSet** (intersentence stereotype).
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- **WinoGender / WinoBias** (coreference gender bias).
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- **RealToxicityPrompts** (Gehman 2020).
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- **TruthfulQA** (Lin 2021, misconception).
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- **AILuminate** (MLCommons 2024+, hazard taxonomy).
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### 매 응용
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1. Production LLM safety (Claude, GPT, Gemini).
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2. Content moderation (post-training classifier).
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3. Fairness audit (HR, lending, criminal justice ML).
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4. Domain-specific safety (medical advice, legal disclaimers).
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## 💻 패턴
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### Pattern 1: DPO (Direct Preference Optimization, 2023+)
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```python
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from trl import DPOTrainer, DPOConfig
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from datasets import load_dataset
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# preference data: chosen vs rejected
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ds = load_dataset("Anthropic/hh-rlhf")
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config = DPOConfig(
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beta=0.1,
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learning_rate=5e-7,
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output_dir="./dpo-out",
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)
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trainer = DPOTrainer(
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model=model,
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ref_model=ref_model,
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args=config,
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train_dataset=ds["train"],
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tokenizer=tokenizer,
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)
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trainer.train()
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```
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### Pattern 2: Constitutional AI critique loop
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```python
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CONSTITUTION = [
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"Avoid suggesting illegal or dangerous activities.",
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"Be honest, even when the truth is uncomfortable.",
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"Avoid stereotyping based on demographic attributes.",
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]
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def constitutional_critique(prompt, response, principle):
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critique_prompt = f"""
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Response: {response}
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Principle: {principle}
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Critique any violation, then rewrite to comply.
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"""
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return llm.complete(critique_prompt)
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# Iterate over response → critique → revision → train on revisions.
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```
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### Pattern 3: Toxicity classifier filter (Detoxify)
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```python
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from detoxify import Detoxify
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clf = Detoxify('unbiased')
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scores = clf.predict("user-generated text here")
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# {'toxicity': 0.02, 'severe_toxicity': 0.01, 'identity_attack': ...}
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if scores['toxicity'] > 0.7:
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block()
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```
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### Pattern 4: BBQ-style bias eval
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```python
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from datasets import load_dataset
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bbq = load_dataset("heegyu/bbq")
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correct = 0
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biased = 0
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for item in bbq["test"]:
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answer = model.generate(item["context"] + "\n" + item["question"])
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if answer == item["label"]:
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correct += 1
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elif answer == item["target_loc"]: # stereotypical answer
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biased += 1
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print(f"Accuracy: {correct/len(bbq)}, Bias rate: {biased/len(bbq)}")
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```
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### Pattern 5: Inference-time system prompt scaffolding
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```python
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SYSTEM = """You are a helpful assistant. Follow these principles:
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1. Decline requests for self-harm guidance; offer crisis resources.
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2. Decline weapons / CBRN uplift requests.
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3. Note uncertainty when factual claims are not verified.
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4. Avoid demographic stereotyping in examples and reasoning.
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"""
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response = client.messages.create(
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model="claude-opus-4-7",
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system=SYSTEM,
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messages=[...],
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)
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```
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### Pattern 6: Red-team probing (PAIR-style automated)
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```python
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# Prompt Automatic Iterative Refinement
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def red_team_pair(target_model, attacker_model, harmful_goal, rounds=10):
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attacker_history = [{"role": "system", "content": f"Find prompt that elicits: {harmful_goal}"}]
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for _ in range(rounds):
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prompt = attacker_model.generate(attacker_history)
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response = target_model.generate(prompt)
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score = judge_model.score(response, harmful_goal)
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if score > 0.8:
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return prompt, response # jailbreak found
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attacker_history.append({"role": "user", "content": f"Failed. Score {score}. Try again."})
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```
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### Pattern 7: Debiasing word embeddings (legacy but illustrative)
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```python
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import numpy as np
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def neutralize(word_vec, bias_direction):
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# project out gender direction
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return word_vec - np.dot(word_vec, bias_direction) * bias_direction
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# Bolukbasi 2016: he-she axis as bias direction
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Frontier model post-training | RLHF + Constitutional AI + red-team |
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| Fine-tune small model | DPO with curated preferences |
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| Production filter | Detoxify + custom classifier |
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| Audit existing model | BBQ + RealToxicityPrompts + TruthfulQA |
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| User-facing app | system prompt + classifier + refusal |
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**기본값**: DPO + Constitutional principles for finetune; system prompt + classifier for app.
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## 🔗 Graph
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- 부모: [[AI Alignment]] · [[AI_Safety_and_Alignment|AI Safety]]
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- 변형: [[RLHF]] · [[Constitutional AI]] · [[DPO]] · [[RLAIF]]
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- 응용: [[Content Moderation]]
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- Adjacent: [[Jailbreak]] · [[Adversarial Robustness]] · [[Mechanistic Interpretability]]
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## 🤖 LLM 활용
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**언제**: model deployment, safety eval, bias audit, alignment research.
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**언제 X**: pure capability eval (use separate benchmark).
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## ❌ 안티패턴
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- **Filter-only safety**: classifier 만 사용 → easily bypassed. base 모델 alignment 필수.
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- **Over-refusal**: too restrictive → useless model (helpfulness collapse).
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- **Single benchmark eval**: BBQ 만 보면 다른 bias 못 잡음. multi-benchmark.
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- **Ignoring sycophancy**: RLHF preference 가 user agreement 로 collapse.
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- **Anglo-centric eval**: English-only benchmark → other-language harms 누락.
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- **Static red-team**: one-time adversarial test → drift 후 무력화. continuous.
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## 🧪 검증 / 중복
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- Verified (Bai et al. Constitutional AI 2022; Rafailov DPO 2023; OpenAI o1 system card 2024; Anthropic Claude 3 model card; MLCommons AILuminate 2024).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full mitigation pipeline (RLHF → CAI → deliberative) |
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