AI Governance Engineer
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Supervised learning

By Updated

Learning from labelled examples 1. Labels encode past human decisions with their errors and bias, so the data card records label provenance and the eval gate tests error rates by subgroup.

Developed in
ch. 11, By learning paradigm
Chapters
ch. 11, AI Defined
Contrast with
Unsupervised learning
Source
1 numbered reference, listed below

Where it is used

The term is not used under this name in running prose; the sections listed under "Developed in" treat it.

  • Machine learning

    The branch of AI in which a system improves at a task by learning patterns from data rather than by following rules people wrote.

  • Reinforcement learning

    Learning to maximise a reward signal through trial and feedback.

  • Reinforcement learning from human feedback (RLHF)

    A way to align a pre-trained model: supervised fine-tuning on human demonstrations, then reinforcement learning against a reward model trained on human…

  • Reward hacking

    A system finding an unintended way to maximise its reward or objective without doing what its designers meant.

  • Self-supervised learning

    Learning by predicting parts of the input itself, such as the next token, over large corpora; the AI Act's definition of a general-purpose model names…

  • Data card

    Structured, versioned documentation of a dataset (provenance, lawful basis, rights, composition and known limitations) maintained as code alongside the system.

  • Eval gate

    A pipeline stage that fails the build when an eval fails; the mechanism that turns an evaluation into an enforced control rather than a report.

Sources

  1. [1] ISO/IEC 22989:2022, Artificial intelligence concepts and terminology (referenced by identifier only; autonomy and heteronomy; clause 5.11 machine learning approaches: supervised, unsupervised, semi-supervised, reinforcement). ISO/IEC. 2022-07. https://www.iso.org/standard/74296.html (verified: secondary)

Definitions of legal terms paraphrase the cited text, which governs. Dated statements are as of .

Cite this term

García Aibar, J. (2026). Supervised learning. In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0), Glossary. https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/glossary/supervised-learning. CC BY 4.0

BibTeX

@misc{aige2026supervisedlearning,
  author  = {Jorge García Aibar},
  title   = {{Supervised learning}},
  note    = {Glossary, AI Governance Engineering: The Thesis \& Body of Knowledge, version 0.5.0},
  year    = {2026},
  doi     = {10.5281/zenodo.22956197},
  url     = {https://aigovernanceengineer.com/glossary/supervised-learning}
}