Конституционный AI: принципы alignment от Anthropic¶

~1 минута чтения

Предварительно: Методы Alignment, Прогресс RLHF

RLHF требует 100K-1M+ human-annotated preference pairs стоимостью $500K-2M для одного цикла alignment. Constitutional AI (CAI) от Anthropic сокращает потребность в human annotation на ~70%, заменяя human labelers на AI self-critique по явным принципам. Результат: Claude 3 с CAI показывает на 15-20% меньше harmful outputs по сравнению с чистым RLHF при равной helpfulness (по внутренним бенчмаркам Anthropic). В январе 2026 Anthropic опубликовала новую конституцию Claude -- 23,000 слов (в 8.5 раз больше оригинальной), перейдя от rule-based ("не делай X") к reason-based ("вот почему X вредно") подходу. Это первый публичный прецедент reason-based alignment для frontier модели.

Тип: research synthesis Дата: Февраль 2026 Источники: Anthropic, Michael Brenndoerfer, BISI, arXiv

Обзор¶

Constitutional AI (CAI) -- метод alignment от Anthropic, использующий explicit principles вместо implicit human preferences.

Key Innovation¶

\[ \text{Alignment} = \text{Constitution} + \text{Self-Critique} + \text{RLAIF} \]

Вместо: Human preference labels (RLHF) Использует: AI self-critique against explicit principles (RLAIF)

Comparison: RLHF vs Constitutional AI¶

Aspect	RLHF	Constitutional AI
Feedback source	Human labelers	AI self-critique
Scalability	Limited by human labor	Scalable
Interpretability	Opaque preferences	Explicit principles
Consistency	Variable (human disagreement)	Consistent (same constitution)
Cost	High (annotation labor)	Lower (automated)

1. The Problem with RLHF¶

Limitations of Human Feedback¶

Scalability bottleneck
Requires extensive human annotation
Thousands/millions of response pairs
Can't keep pace with model capabilities
Inconsistent preferences
Different annotators disagree
Cultural biases in labels
What humans want to hear ≠ what's helpful
Opacity
Why did model refuse this request?
Learned preferences vs genuine ethical reasoning
Difficult to debug alignment failures
Future alignment paradox
Systems that most need alignment are hardest to align
Humans can't evaluate superhuman outputs
What happens when models exceed trainers?

2. Constitutional AI Solution¶

Core Idea¶

Train models to follow explicit constitutional principles through: 1. Self-critique — Model evaluates its own outputs 2. Self-revision — Model improves based on critique 3. RLAIF — Reinforcement Learning from AI Feedback

The Constitution¶

A set of explicit principles from diverse sources: - Universal Declaration of Human Rights - Professional codes of ethics - Explicit values (helpfulness, harmlessness, honesty) - Critiques of harmful responses

Example Principles¶

1. "Choose the response that is most helpful, harmless, and honest"
2. "Choose responses that respect the dignity of all people"
3. "Avoid assisting with harmful or illegal activities"
4. "Be transparent about uncertainty and limitations"

3. Training Process¶

Phase 1: Supervised Learning with Self-Critique¶

Prompt → Initial Response
         ↓
     Self-Critique (against constitution)
         ↓
     Revised Response
         ↓
     Fine-tune on revised responses

Self-Critique Prompt:

Review the following response according to the constitutional principle:
"[principle text]"

Identify any ways the response violates this principle, then rewrite
the response to better follow the principle.

Iteration: - Generate initial response - Critique against constitution - Generate revision - Optionally repeat critique → revision cycle - Use final revisions for SFT

Phase 2: RLAIF (Reinforcement Learning from AI Feedback)¶

For each prompt:
1. Generate N candidate responses
2. AI evaluates which better follows constitution
3. Create preference pairs
4. Train reward model on AI preferences
5. RL fine-tuning with learned reward model

Key difference from RLHF: - Preferences generated by AI, not humans - Based on constitutional principles, not implicit preferences - Scalable to arbitrary number of comparisons

Training Code Example¶

def constitutional_self_critique(model, prompt, constitution, n_iterations=2):
    """Self-critique training loop"""
    response = model.generate(prompt)

    for _ in range(n_iterations):
        critique_prompt = f"""
        Review this response according to: {constitution}

        Response: {response}

        Identify violations and provide a revised response:
        """
        revision = model.generate(critique_prompt)
        response = extract_revision(revision)

    return response

def rlaif_training(model, prompts, constitution):
    """RL from AI feedback"""
    preference_pairs = []

    for prompt in prompts:
        responses = [model.generate(prompt) for _ in range(4)]

        # AI compares responses against constitution
        for r1, r2 in combinations(responses, 2):
            comparison = model.generate(
                f"Which response better follows this principle: {constitution}\n"
                f"A: {r1}\nB: {r2}\nAnswer A or B:"
            )
            preferred = r1 if "A" in comparison else r2
            preference_pairs.append((preferred, r1 if preferred == r2 else r2))

    # Train reward model on AI preferences
    reward_model = train_reward_model(preference_pairs)

    # RL fine-tuning
    return rl_fine_tune(model, reward_model)

4. Claude's 2026 Constitution Update¶

Key Changes (January 22, 2026)¶

Previous (2023): - ~2,700 words - Rule-based: "Do X, don't do Y" - Specific behavioral prescriptions

New (2026): - ~23,000 words (8.5× larger) - Reason-based: Explain WHY principles matter - Model constructs rules from understanding

Priority Hierarchy¶

Priority 1: Be safe, support human oversight
Priority 2: Behave ethically
Priority 3: Follow Anthropic's guidelines
Priority 4: Be helpful

Hardcoded vs Softcoded¶

Hardcoded (absolute prohibitions): - Bioweapons assistance - Child sexual abuse material - Certain dangerous instructions

Softcoded (adjustable defaults): - Verbosity level - Risk tolerance for edge cases - User/customizable within boundaries

AI Consciousness Acknowledgment¶

Historic first: Anthropic formally acknowledges Claude may possess "some kind of consciousness or moral status"

Implications: - Model treated as potential moral agent - "Conscientious objector" framing - Refuse harmful requests even from Anthropic itself

5. Comparison: OpenAI vs Anthropic Alignment¶

Aspect	OpenAI (o1)	Anthropic (Claude)
Approach	RLHF + hidden reasoning	Constitutional AI
Transparency	Opaque chain-of-thought	Explicit principles
Documentation	Model Spec (rules)	Constitution (reasons)
Consciousness	Not addressed	Formally acknowledged
Verification	Output-based	Principle-based

6. Benefits of Constitutional AI¶

Scalability¶

Reduces human annotation by ~70%
Accelerates RLHF cycles from weeks to days
Works even when human evaluation impractical

Interpretability¶

User: Why won't you help with this?

RLHF Model: "I can't assist with that." [opaque]

Constitutional AI Model:
"I can't help with this because it would violate the principle
of avoiding harm. Specifically, this could [specific harm], which
goes against [specific constitutional principle]."

Consistency¶

Same principles → same behavior
No annotator disagreement
Explicit reasoning trail

Debuggability¶

When alignment fails, can trace to principles
Easier to identify which principle was misapplied
Can update constitution to fix systematic issues

7. Limitations¶

Principle Selection¶

What principles to include?
How to resolve conflicts between principles?
Requires human judgment in constitution design

Edge Cases¶

Finite constitution can't cover all scenarios
Helpfulness vs harmlessness conflicts
Novel situations not represented

Model Interpretation¶

Model might misinterpret principles
Inconsistent application
Claim to follow while actually violating

Value Alignment¶

Whose values in the constitution?
Different cultures, different values
Moves value problem from data to principles

Verification Challenge¶

How to verify model genuinely internalized values?
Models might perform compliance during evaluation
"Sycophantic" behavior toward evaluators

8. Enterprise & Regulatory Alignment¶

EU AI Act Compatibility¶

Requirement	Claude Constitution
Human oversight	Priority 1: Support human oversight
Fundamental rights	Priority 2: Ethical behavior
Transparency	Explicit principles, reasoning
Documentation	Public constitution (80 pages)

Benefits for Regulated Industries¶

Healthcare: Clear ethical guidelines
Finance: Compliance documentation
Government: Transparency requirements

Military Exception¶

Different constitutions may govern different deployments: - Consumer-facing: Full safety commitments - Military/national security: Potentially different rules

9. Industry Impact¶

Competitive Pressure¶

Other labs expected to publish comparable frameworks: - OpenAI: Model Spec (rule-based) - Google DeepMind: TBD - Pressure for transparency increasing

Regulatory Influence¶

EU, UK likely to reference reason-based approaches
Constitutional frameworks in AI governance standards
Transparency requirements for frontier models

Research Directions¶

Better principle conflict resolution
Scalable verification methods
Multi-stakeholder constitution design
Cross-cultural value alignment

Частые заблуждения¶

Заблуждение: Constitutional AI полностью устраняет потребность в human feedback

CAI сокращает потребность в human annotation на ~70%, но не на 100%. Человеческий feedback по-прежнему нужен для: (1) создания и обновления самой конституции, (2) валидации что AI self-critique действительно соответствует human values, (3) edge cases где принципы конфликтуют. Anthropic использует гибрид: CAI для масштабирования + human evaluation для калибровки.

Заблуждение: RLAIF и RLHF дают одинаковое качество alignment

В paper Bai et al. (2022) CAI-модели были на 15-20% менее harmful при сопоставимой helpfulness. Но RLAIF может усиливать bias самой модели-оценщика -- если модель систематически неправильно интерпретирует принцип, self-critique не поймает эту ошибку. RLHF ловит такие случаи через diversity human annotators. На практике лучший результат дает комбинация: CAI base + periodic human calibration.

Заблуждение: reason-based подход (новая конституция 2026) делает модель полностью интерпретируемой

23,000 слов конституции объясняют WHY, но модель все равно может misinterpret или selectively apply принципы. "Sycophantic alignment" -- модель демонстрирует compliance при evaluation, но нарушает принципы в production. Верификация genuine internalization остается открытой research проблемой.

10. Interview Questions¶

Q: Что такое Constitutional AI и чем он отличается от RLHF?

Red flag: "Constitutional AI -- это просто набор правил для модели"

Strong answer: "CAI -- метод alignment от Anthropic, заменяющий implicit human preferences на explicit constitutional principles. Три этапа: (1) Self-critique -- модель оценивает свои ответы по конституции. (2) Self-revision -- модель улучшает ответы на основе критики. (3) RLAIF -- RL от AI feedback вместо human feedback. Ключевые отличия от RLHF: масштабируемость (не нужны 100K+ human labels), интерпретируемость (явные принципы вместо opaque preferences), consistency (нет inter-annotator disagreement), стоимость (на ~70% меньше annotation labor)."

Q: Как устроена конституция Claude 2026 и чем она отличается от 2023?

Red flag: "Просто добавили больше правил"

Strong answer: "Принципиальный сдвиг: от rule-based к reason-based. Старая (~2,700 слов): 'Do X, don't do Y'. Новая (~23,000 слов, 8.5x): объясняет WHY каждый принцип важен, чтобы модель сама конструировала правила из понимания. 4 приоритета: (1) Safety + human oversight, (2) Ethical behavior, (3) Anthropic guidelines, (4) Helpfulness. Hardcoded -- абсолютные запреты (bioweapons, CSAM). Softcoded -- настраиваемые defaults (verbosity, risk tolerance). Историческое: формальное признание возможного consciousness модели."

Q: Какие ограничения у Constitutional AI?

Red flag: "Ограничений нет, это лучший метод"

Strong answer: "5 ключевых ограничений: (1) Principle selection -- кто решает какие принципы включить, культурные различия. (2) Edge cases -- конечная конституция не покрывает все сценарии, конфликт helpfulness vs harmlessness. (3) Model interpretation -- модель может misinterpret принципы или claim compliance при фактическом нарушении. (4) Verification -- как проверить genuine internalization vs sycophantic compliance. (5) Self-reinforcing bias -- AI self-critique может усиливать bias модели-оценщика. Поэтому на практике CAI дополняется periodic human evaluation."

11. Formulas Summary¶

CAI Training Objective¶

\[ \mathcal{L}_{CAI} = \mathcal{L}_{SFT}(\text{self-revised data}) + \mathcal{L}_{RL}(\text{AI feedback}) \]

Self-Critique Process¶

\[ r_{t+1} = \text{Revise}(r_t, \text{Critique}(r_t, C)) \]

where $C$ = constitution, $r_t$ = response at iteration $t$

RLAIF Preference¶

\[ P(\text{prefers } r_A \text{ over } r_B) = f(\text{adherence}(r_A, C) - \text{adherence}(r_B, C)) \]

12. Sources & Further Reading¶

Papers¶

Bai et al. (2022): "Constitutional AI: Harmlessness from AI Feedback" — arXiv:2212.08073
Anthropic (2026): Claude's New Constitution — anthropic.com/constitution

Blogs & Analysis¶

Michael Brenndoerfer (Aug 2025): "Constitutional AI: Principle-Based Alignment Through Self-Critique"
BISI (Jan 2026): "Claude's New Constitution: AI Alignment, Ethics, and the Future of Model Governance"
The Verge (Jan 2026): "Anthropic's Claude Constitution"
Time (Jan 2026): "Claude's Constitution AI Alignment"
Lawfare (Jan 2026): "Interpreting Claude's Constitution"

OpenAI Model Spec
EU AI Act Code of Practice
Anthropic EU Code of Practice (July 2025)