Overview

AI alignment is the research program concerned with ensuring AI systems reliably pursue goals and exhibit behaviors that are safe, beneficial, and consistent with human values. The core challenge is that capable AI systems may pursue unintended objectives — not from malice but from the difficulty of specifying human values precisely in a training objective.

Alignment research spans technical methods (how to train models to behave well) and governance questions (what institutional structures ensure safety). It is distinct from AI capabilities research, though the two interact strongly: more capable models create both larger benefits and larger alignment challenges.

Key research directions

Constitutional AI (Anthropic)

A technique in which a model is given a written “constitution” — a set of principles it should embody — and trained through a cycle of self-critique and revision. The model is prompted to evaluate its own outputs against constitutional principles and to rewrite responses that violate them. This produces a model that is both helpful and harmless without requiring human labelers to evaluate every output. Constitutional AI is the conceptual ancestor of .

The key idea is to use a set of principles, rather than human labelers, to provide the model with feedback on its own outputs. — Anthropic, Constitutional AI paper (2022)

RLHF (Reinforcement Learning from Human Feedback)

Human raters evaluate model outputs and these ratings are used to train a reward model, which then supervises further fine-tuning via RL. RLHF was the dominant alignment technique before Constitutional AI and remains widely used. Limitations include reward hacking and the cost of large-scale human annotation.

Scalable oversight

How to evaluate AI outputs that are too complex or numerous for humans to reliably assess. Proposed approaches include debate (AI systems argue for competing answers while a human judges), amplification (bootstrapping human oversight through AI-assisted decomposition), and recursive reward modeling.

Constitutional Classifiers

Anthropic’s applied safety technique: separate input/output classifiers trained on constitution-derived synthetic data to detect and block jailbreaks at inference time. See for full detail. Reduces jailbreak success from 86% to 4.4% with minimal overrefusal.

Responsible Scaling Policy

Anthropic’s Responsible Scaling Policy (RSP) defines AI Safety Levels (ASL) that gate model deployment on demonstrated safety measures:

The RSP creates a formal commitment: Anthropic will not deploy a model at a given capability level unless it can demonstrate adequate mitigations. Constitutional Classifiers is positioned as the primary mitigation for ASL-3 jailbreaking risks.

Overrefusal as an alignment failure mode

Safety training and classifiers that are too restrictive produce overrefusal: declining legitimate, harmless queries because they superficially resemble prohibited ones. Overrefusal is itself an alignment failure — the model fails to be genuinely helpful, which is a stated value. The Constitutional Classifiers research explicitly measures and minimizes overrefusal, achieving only a 0.38% increase in refusal rate on benign queries.

Red teaming as an alignment evaluation method

Red teaming — inviting skilled adversaries to attempt to break safety measures — is a key empirical evaluation method in alignment. It differs from automated evaluation in that human red-teamers can discover qualitatively novel attack strategies that automated benchmarks miss. Anthropic ran formal bug-bounty red teams (183 participants, >3,000 hours) and a public demo (339 participants, ~3,700 hours) to evaluate Constitutional Classifiers.

Resources