A human annotator is a person who labels, categorises or reviews raw data samples — text, images, audio or model outputs — to produce the ground-truth or preference signals used to train and evaluate machine learning models. Annotators follow labelling guidelines and, for subjective tasks, their agreement is measured via inter-annotator agreement to assess label quality. Human annotators are central to supervised data annotation pipelines and to collecting human feedback for reinforcement learning from human feedback.