Sustainability & AI
Supervised learning
Machine learning trained on labelled examples, used for tasks such as classifying satellite imagery or predicting emissions.
Definition
Machine learning in which a model learns to map inputs to outputs from labelled examples — such as images tagged with their contents or histories tagged with outcomes.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
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Overview
What it means
The dominant paradigm in applied AI: a training set of input–output pairs defines the task, and the model generalises the pattern to unseen cases. Performance depends on the quality, volume and representativeness of the labels.
How it is used
Sustainability applications include classifying satellite scenes by land cover, screening documents for relevant disclosures, and predicting equipment failure or energy demand from historical records. Labelling the training data is often the most expensive — and most human — part.
Why it matters
Supervised systems inherit the judgements baked into their labels. Whoever defines and applies the categories — what counts as deforestation, a risk, a violation — shapes what the model can see.
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