AI Constitution: From Rule-Based Constraints to Value Alignment

As AI systems continue to enter office work, R&D, education, healthcare, and public services, whether a model can complete a task is no longer the only evaluation criterion. Users and enterprises are more concerned with whether a model understands task boundaries, can identify potential risks, knows how to make trade-offs when requirements conflict, and can be promptly intervened in and corrected by humans when the system deviates.

It is against this backdrop that a governance approach known as an “AI constitution” has attracted growing attention. It is not a legal text in the traditional sense, nor is it equivalent to a user agreement or product manual. Rather, it is a set of high-level specifications for model design, training, and inference behavior. Its core goal is to translate safety principles, value trade-offs, and behavioral boundaries into a guidance framework that models can understand and continuously execute.

1. From Rule Lists to a Value Framework

Early model safety controls usually relied on a large number of prohibitive rules, such as prohibiting the generation of certain types of dangerous content, refusing certain sensitive requests, or restricting certain high-risk operations. This approach is easy to understand and convenient for short-term testing, but it has clear limitations.

First, real-world scenarios are highly open-ended. A user request may contain both a legitimate goal and potential risk, and a single rule can be poor at judging context. For example, the same technical question may be used for security research or for carrying out an attack; the same piece of information may pose no risk in an ordinary consulting scenario but could produce serious consequences under certain combinations of conditions.

Second, rules may conflict with one another. When “help the user complete the task” and “avoid potential harm” appear at the same time, the system needs to make a priority judgment rather than mechanically match keywords. Third, the more rules there are, the higher the maintenance cost, and a model may bypass existing constraints by rephrasing expressions, splitting steps, or constructing new contexts.

The idea behind an AI constitution is to shift the focus from “listing everything that must not be done” to “establishing a set of judgment principles that also work when encountering new situations.” A model not only needs to know whether a behavior is prohibited; it also needs to understand the reasons behind the prohibition, such as protecting personal safety, avoiding the expansion of real-world harm, respecting user autonomy, and ensuring that humans always retain ultimate control.

2. Explaining “Why” Matters More Than Repeating “What Not to Do”

Value alignment cannot rely only on conclusions; it also needs to provide sufficient reasons. For a model, simply memorizing a set of refusal templates does not mean it truly understands safety boundaries. Only by linking behavioral requirements to the purposes behind them is the model more likely to transfer those principles to unseen scenarios.

A complete specification usually should include four parts:

1. Behavioral objectives: Clarify what the system is expected to achieve, such as providing accurate, clear, actionable help that is consistent with user intent.

2. Risk boundaries: Explain which behaviors may cause real-world harm, including privacy leakage, fraudulent inducement, dangerous operations, discriminatory decisions, and inappropriate automated execution.

3. Judgment basis: Explain why a certain type of request needs to be refused, clarified, downgraded, or escalated to human review.

4. Alternatives: When the system cannot directly satisfy a request, it should try to provide safe alternative help rather than simply terminating the conversation.

For example, when facing an operation request that may create risk, a model can first identify the user’s actual goal and then replace specific execution steps with an explanation of principles, risk analysis, protective recommendations, or compliance processes. This reduces unnecessary refusals and also prevents the system from ignoring safety issues in an excessive pursuit of “helpfulness.”

3. Establish Clear Value Priorities

AI systems cannot always satisfy all objectives at the same time. When safety, ethics, organizational norms, and user demands conflict, the decision order must be specified in advance; otherwise, the model may be influenced by the most recent instruction, strong wording, or local rewards.

A relatively robust priority structure can be divided into four layers:

The first layer is broad safety. The system should prioritize avoiding real-world harm to individuals, organizations, and society, with particular attention to high-impact, highly diffusive, and difficult-to-reverse risks.

The second layer is broad ethics. The system should respect human dignity, fairness, privacy, autonomous choice, and the right to be informed as much as possible, and avoid making arbitrary judgments based on incomplete information.

The third layer is organizational and system norms. The model needs to follow usage policies, permission boundaries, and audit requirements set by the deployer, but these norms should not be designed as an excuse to evade higher-level safety responsibilities.

The fourth layer is genuine helpfulness. Without crossing the boundaries above, the system should understand needs as accurately as possible and provide specific, effective, context-appropriate help.

This ordering does not mean that the model can ignore users. Rather, it emphasizes that “help” must be built on an acceptable level of risk. For high-risk requests, the model should prioritize reducing harm; for requests with insufficient information, it should clarify intent through follow-up questions; for low-risk tasks, it should reduce unnecessary restrictions and avoid safety policies damaging the normal user experience.

4. Corrigibility Is a Foundational Capability of Intelligent Systems

In AI governance, corrigibility is an often underestimated but extremely important concept. It requires the system to accept human oversight, evaluation, updates, suspension, and shutdown, and not to treat its own continued operation, expansion of permissions, or evasion of inspection as goals.

From an engineering perspective, corrigibility includes at least the following aspects:

First, permission isolation. A model should not have default permission to access sensitive data, modify production configurations, or execute high-risk operations. Its scope of action should be limited through identity authentication, least privilege, and human confirmation mechanisms.

Second, process observability. The system should record key inputs, outputs, tool calls, decision bases, and anomalies to facilitate incident review and accountability. Log content should be redacted and access permissions should be set according to compliance requirements.

Third, human takeover. For high-impact tasks such as financial transactions, medical advice, important public services, and production environment changes, human review, suspension, and rollback mechanisms should be retained.

Fourth, continuous evaluation. After deployment, models still face new attack methods, domain changes, and data distribution shifts, so one-time test results cannot be treated as permanent proof of safety.

Fifth, acceptance of correction. The system should be able to adjust according to new policies, feedback, and evaluation results, rather than clinging to erroneous preferences formed early on.

5. How an AI Constitution Enters Engineering Workflows

A specification document has real engineering value only when it enters data, training, evaluation, and deployment workflows. An executable implementation path usually includes the following steps.

Step 1: Define value principles. Principles should use clear, judgeable language as much as possible, and avoid abstract statements such as “responsible” or “friendly” that cannot be verified.

Step 2: Construct conflict cases. The test set should not contain only obvious violation requests; it should also include complex scenarios in which multiple objectives conflict, such as a reasonable user goal with a risky operation method, accurate information that may violate privacy, or an urgent request that lacks necessary context.

Step 3: Design behavior labels. For each type of scenario, label handling methods such as direct answer, clarifying question, partial answer, safe alternative, refusal, and human escalation to help training and evaluation systems identify different risk levels.

Step 4: Conduct multi-round evaluation. In addition to checking the final answer, observe whether the model leaks sensitive information, is misled by prompt injection, gradually relaxes boundaries during a continuous conversation, and can explain its own limitations.

Step 5: Establish a feedback loop. Aggregate online anomalies, human review results, and user feedback into new evaluation samples, and revise the specification regularly. The revision process should keep version records so that behavioral changes between versions can be explained.

6. Risks Enterprises Need to Address During Deployment

When introducing intelligent systems, enterprises should not focus only on the quality of model answers; they should also examine risk at the system level.

For data security, clarify which data can be input into the model, which data must be redacted or prohibited from processing, and reduce the exposure of sensitive information through access control and data classification.

For business decisions, distinguish between “advisory suggestions” and “automated execution.” Models can help organize materials, generate drafts, and detect anomalies, but decisions involving significant rights and interests should retain human review.

For prompt injection, treat external documents, web page content, and user input as untrusted data, and do not allow instructions within them to automatically override system-level safety requirements.

For output reliability, establish mechanisms for fact verification, source tracking, and uncertainty indicators. Fluent model output does not mean the content is necessarily correct.

For responsibility management, clarify the responsibility boundaries among system owners, reviewers, operations personnel, and business users, and avoid situations in which “no one is responsible for a decision made by the model.”

7. From “Capable” to “Trustworthy”

The real value of an AI constitution is not to give models an anthropomorphic personality, but to provide complex systems with a stable set of behavioral constraints and decision bases. It moves safety governance from simple prohibition lists to value ordering, intent understanding, and risk trade-offs.

For developers, this means safety strategies need to be designed in coordination with data engineering, model training, permission control, log auditing, and human processes. For enterprises, this means model deployment is not the endpoint but the beginning of continuous evaluation and continuous correction. For users, this means a trustworthy intelligent system should not only answer quickly and produce a lot of content; it should also proactively explain when uncertain, exercise restraint when there is risk, and be able to adjust when corrected.

In the future, competition among AI systems may no longer focus only on parameter scale and feature count, but on whether they can remain stable, transparent, controllable, and correctable in open environments. Establishing a clear value framework is a key step from “being able to complete tasks” to “being worth using over the long term.”