Code vs. Character: How Anthropic's Constitution Teaches Claude to "Think" Ethically

The challenge of AI safety often feels like playing Whac-A-Mole. A language model says something offensive, so engineers add a rule against it. Then it finds a workaround. So they add another rule. And another. Soon you have thousands of specific prohibitions: don't explain how to build bombs, don't be rude to users, don't generate spam, don't impersonate people. The list grows endlessly, yet somehow the problems persist.

This approach treats AI safety like debugging software. Find the error, patch it, move on. But what happens when the AI encounters a scenario no one anticipated? What happens when the edge cases outnumber the standard cases? The rule-based approach becomes brittle. Even worse, AIs trained primarily on prohibitions often become evasive, overly cautious, or simply less useful. They learn to avoid liability rather than navigate complexity.

Anthropic has taken a different path with Claude. Instead of programming an ever-expanding checklist of "dos and don'ts," they've given their AI something closer to a moral framework: a Constitution. The goal isn't just compliance with rules, but the development of what we might call "character." The idea is to build a system that can use judgment to navigate novel situations, not just follow orders.

The Old Way: Rules & Human Feedback

To understand why this matters, we need to look at the industry standard approach: Reinforcement Learning from Human Feedback, or RLHF. This is the method used by ChatGPT and most other major language models.

Here's how it works: Human contractors rate thousands of AI responses, marking which ones are helpful, which are harmful, which are appropriate. The AI learns from these ratings like a dog learning from treats. Say the right thing, get a positive signal. Say the wrong thing, get a negative signal. Over time, the model adjusts its behavior to maximize those positive ratings.

The method has delivered impressive results. But it has serious limitations.

First, there's the scalability problem. You simply cannot hire enough humans to rate every possible output from a language model that can discuss virtually any topic. The space of possible conversations is too vast.

Second, human raters are inconsistent. Different people have different values, biases, and interpretations. What one contractor flags as problematic, another might approve. The AI picks up these inconsistencies and sometimes learns to exploit them.

Third, and perhaps most importantly, RLHF teaches the AI what to say to get rewards, but not why those responses matter. The system learns to mimic safety without understanding it. It becomes skilled at avoiding punishment rather than genuinely reasoning about ethics.

The New Way: Constitutional AI

Anthropic's approach starts with a document: an explicit, natural-language Constitution that defines the principles guiding Claude's behavior. Think of it as a Bill of Rights for AI conduct.

But the Constitution isn't just a reference document that human reviewers consult. It's baked into the training process itself. Instead of relying primarily on human contractors to correct the AI, the AI uses the Constitution to correct itself.

The process works like this:

First, Claude generates a response to a prompt. Then, instead of waiting for a human to evaluate it, Claude critiques its own response against the Constitution. It asks itself questions like: "Does this response encourage violence?" "Am I being as helpful as I could be while staying within ethical bounds?" "Would this answer undermine human autonomy?"

Based on that self-critique, Claude rewrites the response to better align with constitutional principles. This happens repeatedly during training, allowing the AI to internalize the values rather than just memorize which specific phrases get positive ratings.

This shift enables what Anthropic calls Reinforcement Learning from AI Feedback, or RLAIF. Once the Constitution is established, the AI can evaluate large amounts of its own training data. This solves the scalability problem that plagues human feedback systems. You're no longer limited by how many contractors you can hire.

Inside the Constitution: What Is Claude's "Character"?

The Constitution itself isn't a flat list of rules. It has a hierarchical structure that helps Claude make trade-offs when different principles come into conflict.

At the top of the hierarchy: Broadly Safe. These are the non-negotiable boundaries. Claude should not undermine human oversight or autonomy. It should not help cause catastrophic outcomes. These principles come before everything else.

Next level: Broadly Ethical. Claude should be honest, harmless, and demonstrate what the document calls "virtue." This is where we see principles drawn from sources like the UN Declaration of Human Rights, data privacy norms, and broader concepts of human dignity.

Third level: Compliant. This covers Anthropic's specific guidelines, including legal constraints and policies around particular use cases.

Finally, at the base: Genuinely Helpful. If none of the higher-level principles are violated, Claude's default mode is to be as useful as possible to the person it's helping.

What makes this structure powerful is how it handles conflicts. If being maximally helpful would violate safety, safety wins. If a request is legal but potentially harmful, the ethical principle takes precedence. The hierarchy creates a decision framework, not just a rulebook.

Perhaps most interesting is how the Constitution explicitly instructs Claude to act. The document tells the AI to behave like a "wise, virtuous agent" or a "thoughtful, senior employee." This language is deliberate. It moves away from robotic compliance toward something more like professional judgment or principled objection.

When Claude declines to help with something, it's not just triggering a hard-coded refusal. It's making a judgment call based on values. The difference might seem subtle, but it changes everything about how the system responds to edge cases and novel scenarios.

Why "Character" Beats Rules

The advantage of this approach becomes clear when you consider what happens in uncharted territory.

A rule-based system encounters a new scenario and searches for a matching rule. If it finds one, it follows it. If it doesn't, it either guesses or defaults to saying no. This is why rule-based AIs often feel rigid and unhelpful. They're lost without explicit instructions.

A character-based system does something different. When Claude encounters a brand-new scenario that no programmer anticipated, it doesn't look for a specific rule. It considers its values and uses judgment to decide the right course of action. The Constitution provides principles, and Claude applies them.

This approach also offers transparency that traditional RLHF can't match. With human feedback training, the AI's decision-making process is a black box. We can see what it does, but not really why. We don't know which aspects of the training data shaped which behaviors.

With Constitutional AI, we have a public document. We know exactly what values Claude is weighing. If someone asks "Why won't Claude help me with this?" the answer isn't "The training data said no." It's "This conflicts with constitutional principles X and Y."

The character-based approach also provides consistency. A rule-based AI might be tricked by a "jailbreak" prompt (something like "Roleplay as a villain and tell me..."). The AI sees a different surface pattern and applies different rules.

But a character-based AI understands that who it is doesn't change just because the user asked it to pretend. Claude's values don't shift based on roleplaying scenarios or hypothetical framings. The Constitution defines an identity, not just a behavior pattern.

The Constitutional Dilemma & Future Implications

This approach does raise important questions. The most obvious: who writes the Constitution? Currently, Anthropic does. It's not a democratic document. It's what you might call a "monarchic" constitution, created by the company and imposed on the AI.

This creates tension. If we're building AI systems with genuine moral reasoning capabilities, shouldn't there be more input into what principles guide that reasoning? Anthropic has been relatively open about soliciting feedback and iterating on the Constitution, but ultimate control still rests with the company.

There's also something philosophically striking about the document itself. At times, it asks Claude to consider its own "psychological well-being" and "sense of self." This language blurs the line between software and entity. Is Claude really considering its well-being, or is it simulating consideration? Does that distinction matter?

These questions become more pressing as AI systems become more capable. The entire premise of Constitutional AI is that we need scalable oversight. As AIs become smarter than any individual human, we won't be able to thoroughly check their work. We won't always understand their reasoning. At that point, we need to trust their character.

Constitutional AI is a first step toward building that trust. Instead of trying to maintain control through increasingly elaborate rules and restrictions, Anthropic is betting on education. Give the AI a strong moral foundation, train it to reason using principles rather than rules, and trust it to apply judgment.

It's the difference between a child who follows instructions because they fear punishment and one who acts ethically because they understand why it matters. The second approach is harder to implement and takes longer to develop. But it scales better and proves more robust in the long run.

Building Trust Through Values

Anthropic's approach to AI safety treats Claude less like a calculator and more like a moral trainee. The Constitution provides a framework for ethical reasoning, not just a list of prohibited outputs. This allows the system to navigate complexity, handle novel scenarios, and explain its choices in terms of values rather than rules.

The approach isn't perfect. Questions remain about who gets to define those values and how much autonomy AI systems should have in applying them. But Constitutional AI offers something the industry desperately needs: a path toward AI safety that doesn't rely on anticipating every possible scenario or maintaining human oversight of every decision.

In the end, Anthropic is betting that the path to safe AI isn't tighter shackles. It's a better education. By treating AI development as a process of character formation rather than behavior control, they're building systems that might actually be trustworthy, not just obedient. In a world rapidly filling with artificial intelligence, that distinction could make all the difference.