What does the term ground truth refer to in AI?

The term "ground truth" in the context of AI refers to the known accurate state of a dataset. It represents the benchmark or reference point against which the performance of an AI model can be measured. Ground truth data is typically obtained through reliable observations, measurements, or verifiable historical records, and is crucial for training, validating, and testing machine learning models.

Having accurate ground truth data allows for effective evaluation of model predictions. When a model makes predictions, these can be compared against the ground truth to assess accuracy, identify biases, and refine the model further. This is essential in various applications, such as image recognition, natural language processing, and other domains where precise outcomes are pivotal for success.

In contrast, hypothetical data used for testing, subjective data interpretations, or the latest model predictions do not serve the same foundational role as ground truth. Hypothetical data may lack the reliability required for accurate model evaluation. Subjective interpretations can introduce bias and variability, making them unsuitable for establishing a definitive standard. Meanwhile, the latest model predictions are merely outputs from the model, which need to be validated against the ground truth to determine their accuracy.