Fairness

Fairness is the context-dependent quality of treating people or groups according to justified criteria, rights, needs, and procedures.

Plan for this page

Understand one concept in plain language and connect it to nearby ideas.

You will leave with: In AI, fairness can concern access, representation, error rates, allocation, process, explanation, appeal, or outcomes. Several mathematical criteria can conflict, so choosing a metric is a social and policy decision as well as a technical one.
Time: 3–8 min
Before you begin: No prerequisite. Keep one real example in mind.
Do this now: Read the explanation, then compare the example with the common misunderstanding.

Prepared next stepExplore fairness across a system

FairnessFairness is the context-dependent quality of treating people or groups according to justified criteria, rights, needs, and procedures.

Alignment
Bias
High-risk use

Plain explanation

In AI, fairness can concern access, representation, error rates, allocation, process, explanation, appeal, or outcomes. Several mathematical criteria can conflict, so choosing a metric is a social and policy decision as well as a technical one.

Example

A hiring process examines whether qualified groups receive comparable opportunity, whether error rates differ, and whether candidates can understand and challenge decisions.

Why it matters

A technically accurate system can still distribute benefits and burdens unjustly. Fairness analysis must include the surrounding institution, alternatives, affected people, and remedies.

Common misunderstanding

Misconception: removing protected attributes automatically makes a model fair. Correction: other features can act as proxies, and unfairness can arise in objectives, processes, or deployment.

Related concepts

Follow relationships, not only the alphabet

AlignmentAlignment is the effort to make an AI system’s behavior accord with specified human intentions, values, rules, or objectives.BiasBias is a systematic tendency in data, measurement, design, model behavior, or institutions that shifts results in particular directions.High-risk useA high-risk use is an application where failure, misuse, or unequal performance can create serious consequences for people, rights, safety, livelihood, or essential services.

Explore fairness across a system

Sources and scope

What supports this object

Artificial Intelligence Risk Management Framework (AI RMF 1.0)U.S. National Institute of Standards and Technology

Treats fairness with harmful-bias management as contextual and sociotechnical; legal duties and acceptable criteria depend on jurisdiction and use.

Open source

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