Safety Policy Enforcement
Labeling and evaluating content against a defined set of harm and misuse policy rules.
Safety policy enforcement applies a written policy — a document defining prohibited or restricted content categories — to real model outputs or user inputs, producing a compliant/violating judgment and often a severity level. It sits above narrower tasks like toxicity annotation or hate speech taxonomy labeling, which each cover 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 borderline requests.
Safety policy enforcement data trains both classifiers that filter content in production and the refusal behavior evaluated under refusal quality.
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.
Related terms
Put this into practice
Browse open AI training roles from Alignerr, Mercor, Outlier, and more.
Browse AI training jobs