Ground truth in artificial intelligence (AI) and machine learning denotes the absolute or “true” information about the data in a model. As a benchmark standard or a reference point, ground truth aids in the training and development of machine learning models by providing accurately labelled data for comparison, verification, and calibration of outcomes. With ground truth as a reference, models can learn, adapt, and hence optimise to improve their predictions or classifications over time.
When creating a predictive or classification model, ground truth functions as collected data labeled with the correct or expected output. For instance, in image recognition, the ground truth would be the correct labelling of each object in the image. Machine learning algorithms are then trained on this data set and their performance is measured by how closely their predictions match the ground truth. In training machine learning models, achieving results that closely align with the ground truth signifies high model accuracy.
Establishing ground truth in AI can often be a challenging endeavor as it requires precise, comprehensive, and representative information, which may often necessitate large-scale data collection and manual annotation. Ground truth remains an indispensable aspect in AI because it underpins the development and validation of robust models. Ground truth ensures that AI-systems continue to learn, adapt, and maintain their accuracy and reliability by continually comparing the outcomes with the established ground truth.
