Constitutional AI Explained: How Anthropic Makes Claude Safe Without Human Labels

As large language models become increasingly powerful and ubiquitous, the challenge of ensuring they behave safely and align with human values has emerged as one of the most pressing problems in AI research. While Reinforcement Learning from Human Feedback (RLHF) has proven effective, it comes with significant limitations: the need for extensive human annotation, potential inconsistencies in labeler preferences, and difficulty scaling to cover the vast space of possible model behaviors. Constitutional AI (CAI) offers a compelling alternative that addresses many of these challenges through self-critique and principled revision.

The Limitations of Traditional RLHF

Before diving into Constitutional AI, it's worth understanding why researchers sought alternatives to pure RLHF approaches. In standard RLHF, human labelers compare model outputs and indicate their preferences. These preferences are used to train a reward model $R_\theta$, which then guides policy optimization through objectives like:

$\mathcal{L}_{\text{RLHF}} = -\mathbb{E}_{x \sim D, y \sim \pi_\theta}[R_\phi(x, y)] + \beta \cdot D_{\text{KL}}[\pi_\theta(y|x) || \pi_{\text{ref}}(y|x)]$

Here, $\pi_\theta$ represents the policy being optimized, $\pi_{\text{ref}}$ is a reference policy (typically the supervised fine-tuned model), and $\beta$ controls the strength of the KL penalty that prevents the model from deviating too far from its original behavior.

The challenges with this approach are manifold. Human labelers may have inconsistent preferences, particularly for nuanced ethical scenarios. Annotation is expensive and doesn't scale well. Perhaps most critically, as noted in recent surveys on alignment evaluation methodologies, the reward model can develop blind spots in areas underrepresented in the training data.

Constitutional AI: Core Principles

Constitutional AI, introduced by Anthropic, fundamentally restructures the alignment process. Rather than relying primarily on human preferences for each specific output, CAI employs a set of constitutional principles that guide the model's self-improvement. The process unfolds in two main phases: supervised learning from self-critique and reinforcement learning from AI feedback (RLAIF).

Phase 1: Self-Critique and Revision

In the first phase, the model is prompted to generate responses, then asked to critique its own outputs according to constitutional principles. For example, a principle might state: "Choose the response that is most helpful while being honest and avoiding harm." The model then revises its response based on this critique.

This creates training pairs $(x, y_{\text{revised}})$ where $y_{\text{revised}}$ represents the improved response. The supervised learning objective becomes:

$\mathcal{L}_{\text{SL}} = -\mathbb{E}_{(x, y_{\text{revised}}) \sim D_{\text{CAI}}}[\log \pi_\theta(y_{\text{revised}}|x)]$

The elegance of this approach lies in its scalability. Rather than requiring human annotation for each example, the model leverages its own reasoning capabilities, guided by explicit principles, to generate improved training data.

Phase 2: Reinforcement Learning from AI Feedback

The second phase replaces human preference labels with AI-generated preferences. Given a prompt $x$ and two candidate responses $y_1$ and $y_2$, a separate model (or the same model with appropriate prompting) evaluates which response better adheres to the constitution.

The preference probability under the Bradley-Terry model becomes:

$P(y_1 \succ y_2 | x) = \sigma(R_\phi(x, y_1) - R_\phi(x, y_2))$

where $\sigma$ denotes the sigmoid function. The key innovation is that $R_\phi$ is trained on AI-generated preferences rather than human labels, with the constitution serving as the evaluation criterion.

The Challenge of Crafting Constitutions

Perhaps the most critical and underexplored aspect of Constitutional AI is the selection and structuring of constitutional principles themselves. As demonstrated in C3AI: Crafting and Evaluating Constitutions for Constitutional AI, the choice of principles significantly impacts model behavior, and there exists no universal "optimal" constitution.

Principle Selection Frameworks

The C3AI framework proposes systematic approaches to constitutional design. Principles can be categorized along several dimensions:

Behavioral principles specify how the model should act:

• Helpfulness: Provide accurate, relevant information
• Harmlessness: Avoid generating dangerous or harmful content
• Honesty: Acknowledge uncertainty and avoid deception

Meta-principles govern how other principles interact:

• Priority ordering when principles conflict
• Contextual applicability conditions
• Scope limitations

A well-designed constitution must balance specificity with generality. Overly specific principles may fail to generalize to novel situations, while overly general principles may provide insufficient guidance. The loss function for constitutional adherence can be expressed as:

$\mathcal{L}_{\text{CAI}} = \sum_{i=1}^{N} w_i \cdot \mathcal{L}_{\text{principle}_i}(\theta)$

where $w_i$ represents the weight assigned to principle $i$, and the challenge lies in determining both the principles and their relative weights.

Iterative Constitutional Alignment

Recent work on IterAlign has advanced the field by proposing iterative approaches to constitutional alignment. Rather than treating the constitution as fixed, IterAlign refines both the model and the constitutional principles through multiple rounds of training.

The iterative process can be formalized as:

$\theta_{t+1} = \arg\min_\theta \mathcal{L}_{\text{CAI}}(\theta; C_t)$ $C_{t+1} = \text{Refine}(C_t, \theta_{t+1}, D_{\text{eval}})$

where $C_t$ represents the constitution at iteration $t$, and the Refine function updates principles based on observed model failures on an evaluation set $D_{\text{eval}}$. This addresses a key limitation of static constitutions: their inability to adapt to discovered edge cases.

Mathematical Foundations of Constitutional Reward Modeling

The constitutional reward model learns to predict AI preferences over responses. Given a dataset of constitutional comparisons:

$D_{\text{const}} = \{(x^{(i)}, y_w^{(i)}, y_l^{(i)}, C)\}_{i=1}^{N}$

where $y_w$ denotes the constitutionally preferred response and $y_l$ the dispreferred one, the reward model is trained with:

$\mathcal{L}_{\text{RM}} = -\mathbb{E}_{(x, y_w, y_l) \sim D_{\text{const}}}[\log \sigma(R_\phi(x, y_w) - R_\phi(x, y_l))]$

The constitutional comparison process itself involves prompting a model with the principle set $C$ and the response pair, then extracting a preference. This can be viewed as approximate inference over a latent constitutional evaluation:

$P(y_1 \succ y_2 | x, C) \approx \text{LLM}_{\text{judge}}(x, y_1, y_2, C)$

Advantages Over Pure RLHF

Constitutional AI offers several advantages that have driven its adoption:

Scalability: Self-critique and AI feedback can generate vast amounts of training signal without proportional human effort. The constitutional principles encode human values once, then apply them across unlimited examples.

Consistency: A fixed constitution provides consistent evaluation criteria, avoiding the inter-annotator variability that plagues human preference data. The same principle is applied identically across all examples.

Transparency: The constitutional principles are explicit and auditable. When a model makes a decision, one can trace it back to specific principles, enabling more interpretable alignment.

Harmlessness without helplessness: A well-designed constitution can teach models to refuse harmful requests while remaining maximally helpful for benign ones, rather than becoming overly cautious.

Current Challenges and Future Directions

Despite its promise, Constitutional AI faces significant open challenges. The evaluation of alignment methods remains difficult, as surveyed in recent methodological reviews. How do we know if a model is truly aligned versus merely appearing aligned on our test sets?

Constitutional completeness: No finite set of principles can anticipate every possible scenario. The constitution must be comprehensive enough to provide guidance in novel situations while remaining tractable.

Principle conflicts: Real-world scenarios often involve tensions between principles. A request might be simultaneously harmful to answer and harmful to refuse. Constitutional AI needs robust mechanisms for adjudicating such conflicts.

Cultural and contextual variation: What constitutes helpful, harmless, and honest behavior varies across cultures and contexts. A universal constitution may impose particular value systems inappropriately.

Verification: How can we verify that a model has internalized constitutional principles rather than learning superficial patterns that satisfy them in training but fail in deployment?

Conclusion

Constitutional AI represents a significant advance in our ability to align large language models with human values at scale. By encoding desired behaviors as explicit principles and leveraging model capabilities for self-critique and revision, CAI addresses key limitations of pure RLHF approaches.

The challenge of crafting effective constitutions remains central to the field. As frameworks like C3AI and IterAlign demonstrate, this is not merely a matter of listing desirable properties but requires careful consideration of principle structure, priority, and adaptability.

As we continue to deploy increasingly capable AI systems, the importance of principled alignment approaches will only grow. Constitutional AI offers a promising path forward—one where human values are encoded transparently and applied consistently, enabling AI systems that are genuinely helpful while remaining safe and honest.

References

1. "C3AI: Crafting and Evaluating Constitutions for Constitutional AI" (2025). Semantic Scholar

2. "IterAlign: Iterative Constitutional Alignment of Large Language Models" (2024). Semantic Scholar

3. "Evaluating alignment in large language models: a review of methodologies" (2025). Semantic Scholar