safety and alignment annotation

Safety Policy Enforcement

Labeling and evaluating content against a defined set of harm and misuse policy rules.

What this means for trainers

Read the policy document, not just the examples — platforms revise policy language often, and annotators who work from memorized examples instead of the current text tend to drift out of compliance without noticing.

Safety policy enforcement applies a written policy, meaning a document defining prohibited or restricted content categories, to real model outputs or user inputs, producing a compliant or violating judgment and often a severity level. It sits above narrower tasks like toxicity annotation or hate speech taxonomy labeling, each of which covers one slice of a broader policy.

Because policies are written in natural language and real content is messy, this work leans heavily on calibration and escalation. A policy that reads clearly on paper still needs annotators to agree on how it applies to sarcasm, fiction, or a request that sits right on the boundary of what is allowed.

Safety policy enforcement data trains both the classifiers that filter content in production and the refusal behavior evaluated under refusal quality. Annotators working this queue are typically graded on how closely their judgment tracks the current policy text rather than on their own instinct about what feels wrong.