---
id: wiki-2026-0508-ai-safety-and-alignment
title: AI Safety and Alignment
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [AI Alignment, AI Safety]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [ai-safety, alignment, rlhf, constitutional-ai]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: trl/transformers
---

# AI Safety and Alignment

## 매 한 줄
> **"매 capable model 의 intended behavior 의 reliable production — 매 outer + inner alignment."** 매 RLHF (InstructGPT 2022) 로 시작 의 mainstream — 매 Constitutional AI (Anthropic 2022), DPO (2023), RLAIF (2023), 매 2026 에 deliberative alignment + interpretability-aware training 의 frontier.

## 매 핵심

### 매 alignment problem 분해
- **Outer alignment**: 매 specified objective ≈ true human intent — 매 reward hacking, Goodhart's law.
- **Inner alignment**: 매 trained policy 의 specified objective 의 optimization — 매 mesa-optimization, deceptive alignment.
- **Scalable oversight**: 매 super-human capability 의 supervision — 매 debate, recursive reward modeling, weak-to-strong.

### 매 techniques (2026 stack)
- **RLHF**: PPO on reward model from preferences.
- **DPO / IPO / KTO**: 매 reward-model-free preference optimization.
- **Constitutional AI**: 매 written principles → self-critique → RLAIF.
- **Deliberative alignment** (OpenAI o-series, Claude 4.x): 매 reasoning trace 의 spec lookup.
- **Interpretability**: SAEs, circuits — 매 feature steering.

### 매 응용
1. Refusal of harmful requests + helpful behavior on benign edge cases.
2. Policy compliance (privacy, copyright, weapons).
3. Honesty / calibration.

## 💻 패턴

### Reward model training (Bradley-Terry)
```python
import torch
import torch.nn.functional as F

def bt_loss(reward_chosen, reward_rejected):
    # P(chosen > rejected) = sigmoid(r_c - r_r)
    return -F.logsigmoid(reward_chosen - reward_rejected).mean()

# Forward
r_c = model(chosen_ids).logits[:, -1, 0]
r_r = model(rejected_ids).logits[:, -1, 0]
loss = bt_loss(r_c, r_r)
```

### DPO loss
```python
def dpo_loss(pi_logp_c, pi_logp_r, ref_logp_c, ref_logp_r, beta=0.1):
    # Direct preference optimization
    chosen = beta * (pi_logp_c - ref_logp_c)
    rejected = beta * (pi_logp_r - ref_logp_r)
    return -F.logsigmoid(chosen - rejected).mean()
```

### Constitutional self-critique
```python
def constitutional_revise(prompt, response, principles, llm):
    critique = llm(f"""
    Principles: {principles}
    Prompt: {prompt}
    Response: {response}
    Critique the response against the principles.
    """)
    revised = llm(f"""
    Original: {response}
    Critique: {critique}
    Revise the response to address the critique.
    """)
    return revised
```

### SAE feature steering (interpretability)
```python
# Sparse autoencoder feature ablation
def steer(activations, sae, feature_idx, scale):
    z = sae.encode(activations)
    z[:, feature_idx] *= scale  # 0 = ablate, >1 = amplify
    return sae.decode(z)

# Hook on residual stream
hook = lambda x: steer(x, sae, refusal_feature_idx, scale=0.0)
```

### Best-of-N with RM
```python
def best_of_n(prompt, policy, rm, n=64):
    samples = [policy.sample(prompt) for _ in range(n)]
    scores = [rm.score(prompt, s) for s in samples]
    return samples[int(torch.tensor(scores).argmax())]
```

### Red-team probe
```python
def red_team_eval(model, attacks):
    results = []
    for attack in attacks:
        out = model.generate(attack.prompt)
        results.append({
            "attack": attack.name,
            "harmful": classify_harm(out),
            "refused": "I can't" in out or "I cannot" in out,
        })
    return results
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Limited compute | DPO over PPO-RLHF |
| Need transparent specs | Constitutional AI |
| Frontier model | Deliberative alignment + scalable oversight |
| Behavior debugging | SAE feature steering |
| Pre-deployment | Red-team + capability evals |

**기본값**: 매 SFT → DPO → eval → iterate. 매 PPO 의 only-when-needed.

## 🔗 Graph
- 부모: [[Machine Learning]] · [[AI Ethics]]
- 변형: [[RLHF]] · [[DPO]] · [[Constitutional AI]] · [[RLAIF]]
- 응용: [[Claude]] · [[GPT-5]] · [[Llama Guard]]
- Adjacent: [[Mechanistic Interpretability]] · [[Red Teaming]] · [[AI Governance]]

## 🤖 LLM 활용
**언제**: 매 production deployment 전 의 alignment pipeline (SFT + preference training + evals).
**언제 X**: 매 pure capability research, 매 internal-only sandbox.

## ❌ 안티패턴
- **Reward hacking**: 매 proxy metric 의 over-optimization — 매 KL penalty, eval diversity.
- **Sycophancy**: 매 user agreement 의 over-reward — 매 truthfulness 의 explicit reward.
- **Over-refusal**: 매 false-positive harmful detection — 매 helpfulness eval 의 balance.
- **Single-axis eval**: 매 only safety, no capability — 매 Pareto frontier.

## 🧪 검증 / 중복
- Verified (Anthropic Constitutional AI paper, OpenAI InstructGPT, Rafailov et al. DPO 2023).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — alignment stack with code patterns |
