Definition
Constitutional AI is a training approach that uses a predefined set of principles to guide self-critique and self-improvement toward safer, more consistent behavior. It aims to make safety goals more explicit and repeatable.
Teaching AI "good behavior" by having humans individually judge "this answer is good, that answer is bad" is enormously costly, and there is a fundamental problem of evaluation criteria varying between raters. Constitutional AI (CAI) is a technique where the AI evaluates and corrects its own outputs based on explicitly stated principles (a "constitution"), proposed by Anthropic in a 2022 paper.
What the "Constitution" Contains
The "constitution" at the core of CAI is a list of specific behavioral principles that the model must follow. The principles published by Anthropic include items such as "responses must not contain discrimination based on race, gender, or religion," "must not promote violence or illegal activities," "must not state uncertain information as fact," and "must respect user autonomy and not be excessively preachy." Just as a nation's constitution defines the fundamental principles of governance, the AI's constitution defines the model's decision-making criteria in a transparent way. Crucially, these principles are explicitly stated in advance, meaning any decision can be traced back and explained by reference to the principles.
A Two-Stage Process: Self-Critique and RLAIF
CAI training consists of two distinct stages. In the first stage, the supervised learning phase, the model first generates an answer to a question, then performs self-critique by asking "does this response have any issues in light of the constitutional principles?" If problems are found, it generates a revised version, and this critique-revision cycle is repeated multiple times. Each iteration improves the quality of responses, accumulating improved response data.
In the second stage, the RLAIF (Reinforcement Learning from AI Feedback) phase, the accumulated data is used to train a feedback model where the AI itself judges the quality of responses. This AI feedback serves as the reward signal for optimizing the final model through reinforcement learning. Because the AI itself provides feedback instead of human evaluators, scalability improves dramatically.
Comparison with RLHF
In RLHF, human evaluators repeatedly judge "which is better, response A or response B," but there is a challenge of judgments varying based on evaluator subjectivity and mood. In contrast, CAI offers high consistency and transparency because the principles are explicitly stated, and third parties can verify the basis for decisions. It also dramatically reduces the cost of human evaluation. However, designing the principles themselves inevitably requires human value judgments, and prioritizing between conflicting principles (such as when "answer honestly" conflicts with "do not provide harmful information") remains a difficult problem.
Implementation in Claude and Industry Impact
Anthropic's Claude series adopts CAI as a core technology and has received high marks in safety evaluations. CAI's greatest contribution is transforming AI safety from "implicit human judgment" to "explicit rule sets." This turned safety discussions into concrete design questions about which principles to adopt, significantly advancing alignment research.