Active Learning
A machine learning approach in which a model selectively requests labels for the most informative data samples to improve performance with minimal annotation effort.
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Related terms
The process of gathering feedback from users, human evaluators, or automated systems to assess AI system performance and provide data for evaluation, improvement, and model refinement.
Error AnnotationThe process of labeling, categorizing, and documenting errors in AI system outputs to support evaluation, debugging, model improvement, and feedback-driven training.
LabelingThe process of assigning structured annotations, categories, ratings, or ground-truth values to data or AI outputs so they can be used for training, evaluation, validation, or continuous improvement of AI systems.
Data CurationThe process of collecting, organizing, cleaning, validating, and maintaining datasets to improve the quality of AI training, evaluation, and feedback pipelines.
Continuous ImprovementAn ongoing process of using evaluation results, feedback, and operational insights to iteratively improve AI system performance and quality.