The process of annotating data with ground-truth labels used for supervised learning, often requiring human annotators to classify, tag, or rate examples.
When people look at pictures or text and add notes to teach the AI what things are — like putting name tags on everything.
The process of humans tagging data with correct answers so AI can learn from it — like marking photos as "cat" or "dog" so the AI learns the difference.
The process of annotating data with ground-truth labels used for supervised learning, often requiring human annotators to classify, tag, or rate examples.
The creation of annotated datasets through human or semi-automated annotation, establishing the supervision signal for model training — subject to inter-annotator agreement, label noise, and annotation guidelines quality.
The production of labeled exemplars from an annotation protocol — a critical bottleneck in supervised learning, addressable via active learning, weak supervision, programmatic labeling functions, and human-in-the-loop workflows with calibrated annotator agreement metrics.
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