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Fairness

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The property that a system's outcomes and errors do not unjustifiably disadvantage people or groups. NIST lists "fair, with harmful bias managed" among its trustworthy characteristics 1; in practice fairness is a chosen, recorded metric (group, individual or counterfactual) with a threshold, not a general claim.

Developed in
ch. 16, Choosing a fairness metric by use case
Chapters
ch. 16, Fairness & XAI
Contrast with
Bias
Source
1 numbered reference, listed below

Where it is used

16 chapters of the Body of Knowledge use the term. Each link opens the first section that does.

Patterns that use this term

10 pattern pages use the term, most mentions first.

  • Trustworthy characteristics (NIST)

    The seven characteristics of trustworthy AI in the NIST AI RMF: valid and reliable; safe; secure and resilient; accountable and transparent; explainable and…

  • Calibration within groups

    The fairness property that, in every group, the people given a score s turn out positive at rate s, so a score means the same thing for everyone.

  • Counterfactual flip test

    A test that changes only a protected attribute in an input, or swaps identity terms in otherwise identical prompts, and measures how often the outcome or the…

  • Fairness gerrymandering

    The failure in which a model satisfies a fairness constraint on each predefined group but violates it on subgroups defined by combinations of attributes; the…

  • Demographic parity

    A group fairness criterion that holds when the rate of positive decisions is equal across groups; the adverse-impact ratio is its ratio form.

  • OECD AI Principles

    The five values-based principles (inclusive growth and well-being; human rights, fairness and privacy; transparency and explainability; robustness, security…

  • Claims register

    The record of every public statement about an AI system's accuracy, fairness, safety or capability: the exact wording, where it appears, and the eval run…

Sources

  1. [1] Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1 (risk tolerance and residual risk; seven trustworthy characteristics; transparency answers "what happened", explainability "how", interpretability "why"; MAP 1.1 intended purposes; MANAGE 1.1 go/no-go determination; profiles). NIST. 2023-01-26. https://doi.org/10.6028/NIST.AI.100-1 (verified: primary)

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

Cite this term

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

BibTeX

@misc{aige2026fairness,
  author  = {Jorge García Aibar},
  title   = {{Fairness}},
  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/fairness}
}