What Is Supervised Learning?
Supervised Learning is a machine-learning approach in which a model is trained on labelled examples — inputs paired with known correct outputs — so that it can predict the output for new, unseen inputs.
Supervised Learning — a machine-learning approach in which a model is trained on labelled examples — inputs paired with known correct outputs — so that it can predict the output for new, unseen inputs.
Supervised learning underpins most classification and prediction systems, from credit scoring to image recognition. Its central governance risk is the labelled training data: if the labels reflect historical bias or are poor quality, the model learns and reproduces those flaws. It contrasts with unsupervised learning, which works without labels.
Source: ISO/IEC 22989:2022 (AI concepts and terminology)
Plain-language explanation
Supervised learning underpins most classification and prediction systems, from credit scoring to image recognition. Its central governance risk is the labelled training data: if the labels reflect historical bias or are poor quality, the model learns and reproduces those flaws. It contrasts with unsupervised learning, which works without labels.
Related terms
See where you stand on AI governance
Take the free 7-question maturity assessment and get a personalised action plan.
Free assessment — 3 minutes →