Constitutional AI: Scaling Alignment with AI Feedback and Explicit Principles
Making an AI harmless the classic way means paid humans reading the worst content the model can produce. Constitutional AI flips that: write the rules down as a public document called a constitution, then let the model critique and rewrite its own answers against those rules, and let an AI judge pick better responses. This topic covers the two stages (SL-CAI and RL-CAI), what a constitution contains, and where the method wins and caps out.
01.The Problem: Who Reads the Horrible Outputs?
Let us start with an uncomfortable question.
Imagine you are training a chatbot to be harmless. The classic recipe for that is RLHF — Topic 145, in one plain sentence: humans rank the model's answers, and the model learns to chase the high rankings.
To feed that recipe you need data. Three kinds of data, actually:
- the worst prompts red-teamers can invent,
- the model's plausible-but-harmful answers to them,
- human judgments about which answer is safer.
So who produces that third line?
Paid contractors reading the worst of the internet, all day, and rating it.
That is RLHF's dirty secret.
It is expensive. It is traumatic for the labelers. And it does not scale with capability.
As the model gets better at writing persuasive junk, the humans must get better at judging it — and faster.
Worse: oversight becomes a bottleneck exactly when models outgrow human review. The systems that need the most checking get the least checking, because the checking is slow and the writing is not.
So the question becomes:
If a model can follow written rules at inference time, can written rules replace human graders at training time?
Constitutional AI (Bai et al., Anthropic, 2022, arXiv 2212.08073) answers: yes.
Two-Stage Constitutional AI ⚖️
Two-Stage Constitutional AI ⚖️
The same text the model is trained to follow is written down; both the revision loop and the AI preferences derive from it — making values auditable and label cost human-free.
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