---
title: "Fairness"
description: "The property that a system's outcomes and errors do not unjustifiably disadvantage people or groups."
canonical: https://aigovernanceengineer.com/glossary/fairness
author: "Jorge García Aibar"
license: "CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)"
doi: https://doi.org/10.5281/zenodo.22956197
version: "0.5.0"
updated: 2026-09-24
---

# Fairness

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](https://aigovernanceengineer.com/bok/fairness-and-explainability#choosing-a-fairness-metric-by-use-case)
- Chapters: [ch. 16, Fairness & XAI](https://aigovernanceengineer.com/bok/fairness-and-explainability)
- Contrast with: [Bias](https://aigovernanceengineer.com/glossary/bias)
- In the glossary chapter: https://aigovernanceengineer.com/bok/glossary#t-fairness

## Sources

[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)