Does FAIR require open data?

No. Data can have access restrictions while their descriptions and access conditions are made clear.

Does FAIR mean fair AI?

No. The acronym concerns digital-resource stewardship, whereas fairness in AI concerns a different set of questions about decisions and impacts.

Is a PDF data dictionary sufficient?

It can help people, but machine-actionable descriptions and other discovery, access and reuse requirements may still be missing.

AI Glossary / Mathematics and optimization / Beginner

FAIR principles

Definition

FAIR principles are guidelines for making digital resources Findable, Accessible, Interoperable and Reusable. They encourage data and descriptions that people and software can discover, retrieve under stated conditions, interpret and use appropriately. In an AI project, FAIR practices help teams understand the datasets behind a model and make future reuse less dependent on the memory of the original creator.

What it means

FAIR concerns stewardship of information, not whether an AI decision treats people fairly. A spreadsheet on a shared drive may be available to colleagues yet difficult to discover, interpret or reuse correctly. Conversely, a restricted dataset can support FAIR practices when its description is discoverable and authorized users have a documented access route. The principles provide direction rather than one universal file format or a guarantee of trustworthy data. Their practical implementation depends on the resource, community and purpose.

How it works

Findability involves persistent identifiers, informative descriptions and searchable records. Accessibility involves a documented retrieval mechanism that can include permission checks, with descriptive records retained when the underlying data disappear. Interoperability depends on shared representations, vocabularies and meaningful links between resources. Reusability requires context such as provenance, usage terms and relevant community conventions. These elements work together: a searchable title helps little if the user cannot distinguish versions or understand the measurements. Machine-readable descriptions allow software to participate in discovery and interpretation instead of requiring a person to decipher every document.

Three practical examples

A forecasting team catalogs historical sales datasets with descriptions of coverage, units, collection methods and access conditions before training new models.
A research group deposits a dataset in a suitable repository and links its descriptive record to associated documentation and software.
An internal analytics team preserves a record explaining why an obsolete dataset was withdrawn, helping colleagues understand references in earlier model reports.

Think of it this way

Think of a well-managed library: a catalog helps locate an item, access rules explain how to obtain it, shared cataloging conventions support understanding, and publication details help determine appropriate use. The analogy has limits. FAIR resources can be processed by software and may include databases, models or other digital objects. A tidy catalog alone cannot establish that their contents are accurate or suitable for a particular decision.

Worked example

Consider a fictional maintenance dataset with 1,000 rows. Initially, the file is named final_new.csv, stored in one analyst's folder, and contains columns named t, v and flag. Another team finds a copy but cannot determine whether t is elapsed time or a timestamp, whether v is volts or vibration, or whether flag marks a fault. The steward creates a repository record with a persistent identifier for this version, a searchable description and explicit column definitions. Suppose v is documented as vibration in millimeters per second and t as seconds since the test began. The record explains the collection procedure, equipment scope, missing-value convention, permitted uses and authorization process. The team then performs four practical checks: can a colleague discover the record; can an authorized colleague retrieve the stated version; can a script read the schema and units; and can a prospective user understand the provenance and usage terms? These checks demonstrate specific improvements, not an official four-point certification. If the data later require withdrawal, the descriptive record can remain and explain their unavailability. None of these actions proves that all 1,000 measurements are correct. Sensor calibration and suitability for another machine type still require separate assessment.

Illustrative case study

In a fictional project, Harbor Equipment wants to reuse maintenance information across departments. Its first inventory finds several similarly named exports with different date ranges. A data steward introduces versioned records and clarifies the meaning of fault labels with the engineers who collected them. A model developer discovers that one export labels scheduled servicing as a fault and excludes it from an equipment-failure experiment. This illustrates how explicit documentation can reveal a mismatch before modeling; it is not a claim of measured business savings or a successful deployed model.

Common misconceptions and mistakes

FAIR does not mean unrestricted public release. Accessibility can involve authentication and authorization, while sensitive content remains protected. A downloadable file is not automatically interoperable: an unexplained code such as 99 can still be ambiguous. A persistent identifier does not make a dataset error-free, and a usage license does not establish that the data fit every purpose. FAIR also differs from algorithmic fairness, reproducibility and security, although good information management can support related work. Do not describe an internal checklist as a universal FAIR certification without identifying a real assessment method.

Business use

Start with a dataset that teams already struggle to reuse. Assign a responsible steward and document its intended purpose, versions, provenance and field meanings. Select a repository and metadata approach appropriate to the domain. Test the access process with an authorized colleague who did not create the data. Ask a second user to interpret a sample without informal help, and investigate discrepancies. Maintain descriptions when versions change or resources are retired. Measure concrete operational outcomes, such as unresolved metadata questions, rather than assuming that adding an acronym to a policy improves data management.

Check your understanding

A dataset has a persistent identifier but no explanation of its units. Is the identifier enough to establish that another team can reuse it correctly?

Answer: No. Identification helps locate the resource, while interpretation also needs information such as units, meanings, provenance and applicable usage conditions.

Related terms

Metadata
Data catalog
Interoperability
Data governance
data provenance
persistent identifier

Learn next

take one internal dataset and ask a colleague to locate its correct version and explain three fields. Record every assumption they must make. Then compare a human-readable description with a structured metadata record, identifying which questions software could answer from each.

Metadata

Sources

Sources: GO FAIR Foundation, FAIR Guiding Principles, https://www.gofair.foundation/fair-principles ; Wilkinson and colleagues, The FAIR Guiding Principles for scientific data management and stewardship, https://doi.org/10.1038/sdata.2016.18 . The foundation's principles were reviewed; access to the original article was unsuccessful. The maintenance dataset, four practical checks and Harbor Equipment case are original illustrations, not a certification scheme.

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