Fair use
A doctrine in US copyright law (17 U.S.C. § 107) permitting certain uses of copyrighted material without the rightsholder's permission, decided case by case through a four-factor balancing test: purpose and character of the use, nature of the copyrighted work, amount and substantiality used, and effect on the market for the original. Not a fixed rule; each new use requires its own analysis.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Data Governance Professional (CADGP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A doctrine in US copyright law (17 U.S.C. § 107) permitting certain uses of copyrighted material without the rightsholder's permission, decided case by case through a four-factor balancing test: purpose and character of the use, nature of the copyrighted work, amount and substantiality used, and effect on the market for the original. Not a fixed rule; each new use requires its own analysis.
A defense in United States copyright law that permits some unlicensed use of a protected work, weighed across four factors (purpose and character of the use, nature of the work, amount used, and effect on the market). In Thomson Reuters v. Ross Intelligence the defense failed because the use was commercial, not transformative, and harmed the market.
A US copyright doctrine allowing limited use of copyrighted material without permission, evaluated under a multi-factor test that, in AI training disputes, has turned heavily on the acquisition method of the copied material and whether the resulting use substitutes for the original work's market.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Copyright and the corpus: what your organization may train on, and the provenance file that proves it · Lineage Under Audit, Certified AI Data Governance Professional (CADGP)
- Who owns the output: IP, training-data provenance, and the liability chain when AI work goes wrong · Evidence Engineering, Certified AI Governance Professional (CAIGP)
- Intellectual Property and AI: Training Data, Outputs, and Patents · Legal Frameworks and Liability Navigation, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where Fair use starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Traces where data came from, what transformed it, who owns each hop and where it flows downstream, so a number can be defended.
One lesson on this page teach it as a primary outcome.
Tracks the policy fight over AI infrastructure: data center siting, grid interconnection, energy and water duties, and who pays for the buildout.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief Data Officer, data leadership editionneeds it at depth
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- Data Governance Leadneeds it at depth · 2 open postings this week
- Privacy Analystneeds working knowledge
- AI Auditorneeds working knowledge · 9 open postings this week
- Third-Party AI Risk Analystneeds working knowledge
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Article 10; NIST AI RMF MAP 4; EU Energy Efficiency Directive data centre reporting.
One interview question about it
Trace the lineage of a training dataset back to its source. What do you record, and what breaks when you cannot?
Asked of candidates for data lineage and provenance. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Privacy and data protection carries it as work you can show: Data maps and records of processing are lineage by another name.
- Data analysis, stewardship and business intelligence carries it as work you can show: Source-to-report lineage is what you trace.
- A data science, statistics or analytics degree carries it as a claim to back up: Lineage and provenance are known ideas, rarely yet documented for an auditor.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.