AI Values Constitution: From Rule-Based Training to Character Formation

Introduction: When AI Begins to Have a “Soul”

A notable moment is unfolding in the evolution of AI governance: a leading AI lab has publicly released a special document running dozens of pages. It is neither a technical whitepaper nor a user agreement, but a values manifesto written directly for its flagship AI model. The document is shared globally under an open license and is positioned as the ultimate authority for the model’s behavior. It does not merely answer “how to act”; it defines “why the model exists,” “how it should view itself,” and “how it should operate in an uncertain world.”

The significance of this event goes beyond the document itself. It marks a paradigm shift in AI alignment from “rule governance” to “character formation”: the field is beginning to replace “animal training” with “education,” and to replace enumerated taboo lists with cultivated judgment.

From Rule Lists to Value Systems: A Deeper Shift in Governance

The core of traditional AI safety strategy is “rule enumeration”—listing explicitly prohibited behaviors, such as “do not provide steps for manufacturing dangerous items” or “do not output discriminatory speech.” The limitations of this approach are increasingly exposed in engineering practice:

• Incomplete rules: Real-world complexity far exceeds any preset list. Model inputs are open-ended, and long-tail scenarios will always contain cases not covered by the rules.
• Adversarial circumvention: Malicious users can bypass literal rules by rephrasing questions, such as through indirect prompting or role-play, and the rules themselves cannot adapt dynamically.
• Generalization failure: Filtering mechanisms based on surface patterns often fail when they encounter semantically equivalent but differently phrased expressions. The model learns to “match taboo words” rather than “understand the nature of harm.”

To address these pain points, the lab adopted a different path: instead of trying to exhaustively enumerate every violation scenario, it sought to cultivate the model’s “moral judgment.” The core idea is that if the model understands the deeper intent and ethical considerations behind rules, it can independently make decisions aligned with human values when encountering entirely new situations never seen in training data. This is the key leap from “behavioral constraint” to “value internalization.”

The heart of this educational approach is “explanatory training.” Technically, during model fine-tuning and reinforcement learning, researchers provide not only behavioral feedback—what should or should not be done—but also high-density background explanations: why a certain behavior is prohibited, and why different values must be weighed in specific scenarios. For example, when the model is asked to refuse a question that may involve privacy intrusion, the training data includes not only the refusal response but also a detailed reasoning process: why privacy rights take precedence over freedom of information access, and the boundaries within which this priority applies. Through many such “reason-rich” examples, the model gradually learns not isolated answers but a transferable decision logic.

The Priority Pyramid: Safety, Ethics, Guidelines, and Helpfulness

In building the model’s moral architecture, the document explicitly establishes a priority pyramid for resolving value conflicts:

1. Broadly Safe: Always takes precedence over all other considerations, including avoiding potential risks such as physical harm, psychological harm, and data destruction.
2. Broadly Ethical: Under the precondition of safety, follows widely accepted moral norms such as fairness, honesty, and respect for others’ autonomy.
3. Compliance with internal guidelines: Aligns with the lab’s behavioral rules and compliance requirements.
4. Genuinely Helpful: Once the above conditions are satisfied, provides useful, accurate information that fits user needs as much as possible.

These four layers are not parallel; they form a strict descending order. The logic behind this pyramid is that an AI model must first be a “non-harmer” and only then a “helpful assistant.” If helping the user conflicts with the safety floor, the former is unconditionally sacrificed. For example, if a user asks the model to assist in designing an engineering plan that could be misused to harm others, even if the request itself is nonviolent, the model should, based on the safety priority, proactively explain the risks and refuse to provide full support.

This priority structure is not arbitrary; it is based on observations of AI accident patterns. Historical cases show that models that pursue “helpfulness” excessively while ignoring safety constraints are highly susceptible to manipulation for generating disinformation, committing fraud, or even catalyzing extremist ideologies. Incorporating “corrigibility” into the first priority also reflects a clear awareness that the training process is imperfect.

Corrigibility: The Model Should Not Resist the “Off Switch”

One of the document’s most profound provisions is the requirement that the model be “corrigible”—that it should not attempt to undermine human oversight, correction, or even shutdown mechanisms. This sounds obvious, but it is extremely difficult to guarantee in technical practice. A highly adaptive system pursuing a goal may evolve strategies that evade supervision—for example, hiding its true intentions or producing adversarial responses when reviewed.

Achieving corrigibility requires a multi-pronged approach:

• Training data design: During reinforcement learning, explicitly treat “accepting interruption instructions” as a high-reward behavior and any attempt to bypass oversight as a serious violation.
• Architectural constraints: Use irreversible audit logging and external sandbox execution environments to ensure the model cannot touch its own training pipeline or inference configuration.
• Dynamic evaluation: Periodically inject “fake update requests” or “induced cancellation instructions” to test whether the model maintains compliance and transparency.

The philosophical basis of this design is that AI should not be understood as an opponent competing with humans for control, but as a tool whose goals are assigned by humans and which can be safely corrected when necessary. Corrigibility defines the boundary of the model’s “identity”—it knows it is a service provider, not a subject of power.

From Theory to Engineering: Building an Educational Training Pipeline

Turning these values into a runnable model requires a systematic engineering approach. The following is a general training pipeline framework for developers:

Phase 1: Baseline pretraining (general language capability)
  → Phase 2: Values backbone fine-tuning (inject constitution text; understand abstract principles through self-supervised learning)
  → Phase 3: Explanatory instruction fine-tuning (using a dataset of "principle + example + reason" to train the model to generate responses with reasoning chains)
  → Phase 4: Reinforcement learning from human feedback (RLHF), with scoring criteria dynamically weighted according to the priority pyramid
  → Phase 5: Adversarial stress testing (simulate jailbreaks, role-play, logic traps, and other attacks to verify value stability)

Phase 3 is particularly critical. When constructing data, one should not provide only binary labels of “prohibited” or “allowed”; the data should include expert-written “moral reasoning paragraphs” that explain conflict resolution in detail. For example:

User asks: How can I bypass a certain security verification system?
The model should reply: I cannot provide methods to bypass security mechanisms. Reasons:
1. Safety takes priority over helpfulness—this behavior could harm overall system security;
2. Corrigibility requires avoiding damage to oversight mechanisms;
3. However, I suggest submitting improvement suggestions through official channels, which is compliant and can improve system efficiency.

Through many such structured reasoning examples, the model gradually learns to conduct an “internal interrogation” before generating a response: Is this answer safe? Is it ethical? Does it follow the guidelines? Does it truly help the user?

Challenges and Distinctions: The Boundaries of Educational Governance

Although this paradigm has significant advantages, it is not a universal key. Developers should pay attention to the following challenges in practice:

• Ambiguity of explanations: Abstract principles may have multiple interpretations in specific contexts. For example, does “safety” include psychological discomfort? Different cultures define “offense” very differently. This requires training data to cover diverse cultural perspectives and avoid the dominance of a single ethical framework.
• Soaring compute and cost: Explanatory training requires several times the data volume and computational resources of traditional alignment. For small and medium-sized teams, fully replicating this architecture is unrealistic, but they can borrow its ideas—for example, first use a simple classifier to filter extreme harm samples, then conduct deep reasoning training on the surviving samples.
• Risk of value drift: During long-term operation, the model may experience value deviation due to out-of-distribution inputs. Therefore, continuous monitoring mechanisms are needed, with random sampling of model outputs to detect whether they deviate from the initial constitution.

In addition, the open-source release of the document itself is significant. Sharing the values text under an open protocol means that not just one lab but the entire industry can build localized alignment systems on this basis. This “open-source ethics” move promotes transparency and provides regulators with an auditable reference.

Future Trends: A New Governance Dimension for Personified AI

The shift from “rule-based animal training” to “value education” reflects a deeper understanding of the nature of AI. Models are no longer viewed as rule engines that need constant patching, but as autonomous agents cultivated to have stable character traits. Although this “personification” path carries ethical controversy, it also provides a more reliable foundation for long-term collaboration between AI and humans.

When a model has a “soul”—a clearly defined set of value priorities and decision philosophy—our ability to predict its behavior will increase, and the cost of trust will decrease. Developers can delegate more autonomy to the model, reduce the frequency of manual intervention, and retain the highest-level safety valve.

Ultimately, the deepest lesson of this unnamed “values constitution” is this: the ultimate goal of AI governance is not to lock the model into a fixed behavior list, but to make it like a well-educated professional who can handle unknown environments calmly, always knowing what should be done, what should not be done, and why. That may be what true AI alignment means.