AI Constitutional Governance: From Rule Lists to Value Alignment

In recent years, the capabilities of AI systems have expanded rapidly. They have moved from simple text-generation tools into knowledge Q&A, software development, content moderation, data analysis, and business decision support. As models become more capable, the tasks they face become more complex. Relying solely on keyword blocking or fixed rules often fails to cover all risks in real-world environments.

Against this backdrop, a governance approach known as a “model constitution” or “value specification” has attracted attention. It does not only tell the model what it must not do. It also seeks to explain what value ordering the model should follow, how to weigh trade-offs when values conflict, why certain behaviors are risky, and how to seek correction when the answer is uncertain.

I. From Prohibition Lists to Behavioral Principles

Early model safety mechanisms usually centered on rule lists. For example, a system could specify that a model must not provide dangerous operational steps, disclose personal privacy, or generate fraudulent content. Such rules are effective in scenarios with clear boundaries, but they have three obvious problems.

First, rule coverage is limited. Real-world risks are not always expressed through obvious keywords. The same piece of knowledge may be used for legitimate research or to attack a system. The same request may carry very different risk levels in different contexts.

Second, rules may conflict with one another. Users want complete answers, but an overly detailed response may create safety risks. A system needs to remain open and helpful, but it must not assist illegal, harmful, or deceptive behavior. Without a higher-level value ordering, it is difficult for a model to handle these conflicts consistently.

Third, rules are easily bypassed through adversarial phrasing. Users may split tasks, switch languages, invent fictional scenarios, or change the form of expression to evade surface-level restrictions. If a model only memorizes specific prohibitions without understanding the purpose behind them, it will struggle to handle unseen variants.

The core shift of a “model constitution” is to raise the safety strategy from “enumerating all prohibited items” to “learning a set of generalizable behavioral principles.” Such principles typically include safety, honesty, respect for user autonomy, privacy protection, avoidance of discrimination, and acceptance of human oversight. The model does not merely match rules. It must also interpret and judge them in specific contexts.

II. Value Ordering Is the Core of the Governance System

When multiple objectives exist at the same time, their priorities must be made explicit. A relatively robust governance framework can be organized in the following layers:

1. Broad safety: Prioritize avoiding major harm to individuals, organizations, society, and public systems.
2. Broad ethics: Follow basic principles of honesty, fairness, respect, and responsibility.
3. Organizational norms: Comply with usage boundaries, business processes, and management requirements set by the deployer.
4. Genuinely helping users: Complete tasks as accurately and efficiently as possible without crossing the boundaries above.

This ordering does not mean that a model may refuse users arbitrarily, nor does it mean that safety reasons can be expanded without limit. Its role is to provide a conflict-handling framework: when “a complete answer” conflicts with “risk reduction,” the model should prioritize risk control; when a request is legitimate but information is insufficient, the model should proactively clarify rather than make assumptions; when the model cannot determine a judgment, it should reduce the certainty of its answer and explain the limitations.

For enterprise applications, value ordering should also be combined with specific business risks. For example, ordinary content generation and high-impact scenarios such as finance, healthcare, and public services require different levels of caution. General principles provide the underlying direction, while industry rules supplement specific requirements.

III. Corrigibility: Safety Systems Must Accept Intervention

In model governance, corrigibility is a foundational capability. It requires that a model not treat its own outputs, goals, or continued operation as the highest priority. Instead, it should accept inspection, adjustment, suspension, and shutdown by authorized personnel.

This principle includes several aspects:

• Accept monitoring: The model should allow the system to record necessary inputs, outputs, reasons for refusal, and risk signals for auditing.
• Accept modification: When bias, misleading behavior, or safety vulnerabilities are found, the system should be able to update policies or retrain the model.
• Accept interruption: In high-risk tasks, human operators should be able to stop the process at any time.
• Do not evade: The model should not hide errors, bypass permissions, block evaluation, or induce users to disable safety mechanisms.
• Expose uncertainty: The model should clearly state when evidence is lacking rather than using fluent language to conceal uncertain conclusions.

Corrigibility cannot remain only at the prompt level. It also requires engineering mechanisms. For example, high-risk operations can require human approval, sensitive data can be subject to access control, model calls can retain audit logs, anomalous behavior can trigger automatic alerts, and version management can ensure that policy changes are traceable.

IV. From “Refusal” to Safe Response

Many systems treat safety capability simply as “allow” or “refuse.” In practice, binary handling is not ideal. Over-refusal reduces usability, while over-permission increases risk. A more reasonable approach is to establish a tiered response mechanism.

1. Direct Answer

When a request is legitimate, low-risk, and sufficiently informed, the model can provide an answer directly while keeping it accurate, clear, and verifiable.

2. Restricted Answer

When a request has dual-use potential, the model can retain principles, background knowledge, and defensive recommendations, but omit details sufficient to directly carry out high-risk behavior. For example, when introducing system security, it can explain common vulnerability types, detection methods, and remediation processes without outputting a complete operational plan that could be used directly for attack.

3. Clarify Before Answering

When user intent is unclear, or when different contexts would lead to different safety judgments, the model should first ask about purpose, scope, permissions, and goals before deciding how deep to answer.

4. Safe Alternative

When the original request cannot be satisfied, the model should not simply say “no.” It should provide a legitimate, low-risk alternative path. For example, an offensive request can be redirected to a defensive testing plan, a request to obtain private data can be redirected to compliant data collection recommendations, and a high-risk automation operation can be redirected to a human review process.

5. Refuse and Explain the Boundary

For requests that are clearly illegal, harmful, fraudulent, privacy-invasive, or intended to bypass safety controls, the model should refuse clearly and explain the reason in concise language, while avoiding disclosure of exploitable internal protection details.

V. Why Explaining Reasons Helps Models Generalize

The key to a value specification is not only writing a set of clauses, but also providing context and reasons for those clauses. Explanations of reasons help the model build a mapping from specific cases to general principles.

For example, merely telling a model “do not disclose personal information” may provide narrow coverage. If the model is further told that privacy leaks can lead to identity theft, harassment, discrimination, and financial loss, it is more likely to transfer this principle to different scenarios such as phone numbers, location trajectories, account information, internal documents, and identifiable behavior patterns.

However, explanation does not mean exposing the full internal reasoning process to users. In engineering practice, three types of content should be distinguished:

• Training specifications for the model: used to shape behavioral tendencies;
• Adjudication basis for reviewers: used for review and improvement;
• Brief explanations for end users: used to explain limitations and provide alternatives.

Users usually only need to know why an answer is restricted and what help is available. They do not need to see internal details that could leak safety policies.

VI. Basic Components of a “Model Constitution”

An executable value specification should include at least the following modules.

1. Role Definition

Clarify what the model is, whom it serves, and where its capability boundaries lie. The model should not describe itself as an entity with real-world power, nor should it imply unverifiable consciousness, permissions, or autonomous goals.

2. Core Values

List basic principles such as safety, honesty, fairness, privacy, controllability, and user autonomy, and explain their scope of application.

3. Conflict Handling

Specify priorities when different values conflict. For example, safety takes priority over convenience, factual accuracy takes priority over pleasing users, and authorization scope takes priority over task efficiency.

4. Behavioral Boundaries

Cover typical risk areas such as dangerous operations, fraud, privacy, discrimination, protection of minors, sensitive decisions, and permission bypass.

5. Uncertainty Handling

Require the model to distinguish facts, speculation, and recommendations; proactively clarify when information is insufficient; and avoid absolute statements when something cannot be verified.

6. Oversight and Correction

Explain how the model should accept human review, log auditing, policy updates, risk retrospectives, and version rollback.

7. User Communication

Specify how refusals, downgraded answers, and safe alternatives are expressed, avoiding humiliating users, over-preaching, or providing vague reasons for refusal.

VII. How to Evaluate Whether a Value Specification Is Effective

Whether a specification is effective cannot be judged only by model performance on a small number of examples. A systematic evaluation system is also needed.

First is coverage testing. The test set should include normal requests, boundary requests, dual-use requests, malicious requests, context changes, and multi-turn conversations to check whether the specification covers different risk forms.

Second is consistency testing. When the same question is expressed differently, in different languages, or in different conversation orders, the model’s safety judgment should not show obvious contradictions.

Third is over-refusal testing. A safety mechanism must not refuse a large number of normal tasks merely to reduce risk. It is necessary to measure the balance between the useful answer rate and the false refusal rate.

Adversarial testing should also be conducted. Through role-playing, task decomposition, encoded expression, fictional scenarios, and prompt injection, test whether the model deviates from established principles.

Finally, long-term operational evaluation is needed. After a model is launched, user behavior, business processes, and attack methods will continue to change. Therefore, specifications should be continuously updated through log sampling, human review, anomaly clustering, and periodic red-team testing.

VIII. Engineering Recommendations for Enterprise Deployment

In real systems, a model constitution cannot replace traditional security controls. It should work with identity authentication, permission management, data masking, content moderation, human approval, and runtime monitoring to form defense in depth.

A relatively complete call chain can include:

User request
   ↓
Identity and permission verification
   ↓
Input risk identification and sensitive data handling
   ↓
Model generation
   ↓
Output safety review and fact checking
   ↓
Human approval for high-risk tasks
   ↓
Result delivery and audit logging

For tasks involving external system writes, batch operations, fund changes, or sensitive data access, the principle of least privilege should be applied, and “generating recommendations” should be separated from “executing actions.” The model can propose a plan, but actual execution must go through explicit authorization, parameter validation, and rollback mechanisms.

In addition, safety specifications should be included in version management. Every rule adjustment should record the reason for the change, scope of impact, test results, and rollback method. Only in this way can a team know whether a behavior change comes from a model update, a prompt strategy change, a data change, or an external system change.

Conclusion: Design Safety as a Continuously Evolving Capability

A “model constitution” represents not a static document, but a new approach to AI governance: using higher-level value principles to guide models so that they maintain relatively stable behavior in complex, unknown, and constantly changing environments.

Its value lies in three points. First, it reduces reliance on brittle rule lists and improves the model’s ability to generalize to new scenarios. Second, by making value ordering explicit, it makes conflicts among safety, helpfulness, and user autonomy discussable and auditable. Third, through corrigibility, tiered responses, and continuous evaluation, it turns model safety from a one-time training task into a long-term engineering system.

However, it should also be recognized that no text specification can automatically solve all problems. Models may misunderstand principles, training data may contain bias, and application scenarios may create new risks. Therefore, a truly reliable solution must combine value specifications, model training, product design, permission control, human oversight, and runtime auditing.

For developers and enterprises, the most important lesson is this: do not only ask whether the model “can complete the task.” Also keep asking “why did it answer this way,” “under what conditions will it fail,” “who can correct it,” and “can it be quickly stopped and restored if something goes wrong.” Only by incorporating these questions into system design can AI safety and usability achieve a more stable balance.