Institutional review board (IRB)
A committee that reviews and approves research involving human subjects, including the collection and use of data such as medical images, to ensure the research meets ethical and consent standards; IRB approval covering AI model training specifically is a strong provenance-test signal for health-related synthetic data.
Defined in 2 GAGE programs, which carry 2 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 required oversight body at nearly every American university and research hospital, governed by federal human-subjects research regulations, that reviews the informed-consent process for any research involving a human participant before that research begins; cited in this topic as a real-world precedent for the three-part informed consent standard.
A committee that reviews and approves research involving human subjects, including the collection and use of data such as medical images, to ensure the research meets ethical and consent standards; IRB approval covering AI model training specifically is a strong provenance-test signal for health-related synthetic data.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Synthetic data: when it protects, when it launders, and how to tell · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- Consent, deletion, and nothing leaves the room · The Demystification Lab, Senior AI Scam Defense
Where it shows up at work
A definition is where Institutional review board (IRB) 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.
Builds minimization, purpose limitation, de-identification, consent and retention into an AI system before launch, with tests that prove it.
One lesson on this page teach it as a primary outcome.
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.
Designs safeguards, family protocols and reporting paths for older adults, minors and people who cannot challenge an automated outcome.
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.
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- Chief Privacy Officer, AI privacy editionneeds it at depth
- Privacy Analystneeds working knowledge
- Chief Data Officer, data leadership editionneeds it at depth
- Data Governance Leadneeds it at depth · 2 open postings this week
- AI Auditorneeds working knowledge · 9 open postings this week
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: ISO/IEC 27701; GDPR Article 25; EU AI Act Article 10; NIST AI RMF MAP 4.
One interview question about it
How do you build privacy into an AI product from the design stage rather than checking it at the end?
Asked of candidates for privacy by design and privacy engineering. 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.