Error Annotation
Also called: Error Labeling
The process of labeling, categorizing, and documenting errors in AI system outputs to support evaluation, debugging, model improvement, and feedback-driven training.
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Related terms
The process of collecting, organizing, cleaning, validating, and maintaining datasets to improve the quality of AI training, evaluation, and feedback pipelines.
Feedback CollectionThe 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.
Evaluation DatasetA curated collection of test examples, inputs, and expected outcomes used to measure the quality, accuracy, safety, and reliability of AI models and systems.
Failure AnalysisThe process of investigating errors, failures, or unexpected behavior in an AI system to identify root causes, assess their impact, and implement corrective or preventive actions.
Human EvaluationAn evaluation method in which human reviewers assess the quality of AI system outputs against defined criteria such as correctness, relevance, helpfulness, safety, or fluency, providing judgments that complement or validate automated evaluation metrics.