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Verità di riferimento (Ground Truth)
Ground truth is the answer or label considered correct against which an AI model's output is compared during evaluation. It represents the objective reference point, established by human experts or verified sources, used to measure a system's accuracy. Without reliable ground truth, any evaluation score loses meaning.
It is built by collecting data labeled by people knowledgeable in the domain, or by deriving it from authoritative, already validated sources, such as official documents or established databases. The quality of the ground truth depends on consistency among annotators and on the clarity of the criteria used to label data. Errors or ambiguity in the ground truth directly affect the reliability of the final evaluation.
It is used to train and validate classification models, to compute metrics like precision and recall, and to build reference benchmarks in the field. Even in tests of language models, ground truth defines which responses are considered acceptable.
The term comes from cartography and remote sensing, where it referred to on-the-ground verification of what was observed from satellite or aerial photographs; it was later adopted by machine learning with the same meaning of a verified, reliable reference.
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