---
title: 'AI fairness'
description: 'The normative and technical treatment of how an AI system distributes errors, benefits, burdens, and opportunities across people or groups.'
canonical_url: 'https://darkfactory.dev/glossary/fairness'
markdown_url: 'https://darkfactory.dev/glossary/fairness.md'
collection: glossary
date_published: '2026-08-03T00:00:00-04:00'
date_modified: '2026-08-26T00:00:00-04:00'
---

# AI fairness


## Definition

AI fairness asks how a system distributes errors, benefits, burdens, and opportunities across people and groups, and whether that distribution is justified. It is both a technical and a normative problem. Data and metrics can measure disparities, but they cannot decide by themselves which differences are acceptable or what a fair outcome should be.

Common criteria include similar error rates across groups, similar positive-outcome rates, and calibration within groups. These criteria answer different questions and can conflict, especially when underlying rates differ. Choosing one is a policy decision with real consequences, not a neutral tuning step.

A defensible fairness assessment names the affected people, the decision being made, the comparison groups, the chosen criterion, important alternatives, and who has authority to accept the tradeoff. It also asks whether the system should make the decision at all.

## Why it is contested

Fairness criteria answer different moral and statistical questions, and they can require incompatible results. Two assessments can measure the same system correctly yet reach different judgments because they choose different groups, error costs, comparison baselines, or definitions of acceptable treatment. Data can expose those choices but cannot resolve them. A fairness claim must therefore name the criterion and the authority behind the tradeoff.

## Distinguish it from nearby terms

Bias is a systematic tendency in data, models, or decisions. Fairness is the standard used to judge whether the resulting treatment is acceptable. Equal aggregate accuracy does not establish fairness if the errors fall differently or carry different costs.

## Check your understanding

A hiring model has equal overall accuracy for two groups but rejects qualified candidates from one group twice as often. The average metric hides the disparity. A fairness review must examine the relevant error and the consequences of that error.

## Related terms

- [AI bias](https://darkfactory.dev/glossary/bias)
- [Accuracy](https://darkfactory.dev/glossary/accuracy)

## Evidence and further reading

- [Google Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/)