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Evidence hierarchy

A six-rung ranking of evidence strength used to decide what to lead with in a design defense: measured data from a tested prototype (strongest), a cited standard or specification, a calculation with stated assumptions, documented precedent, expert judgment or analogy, and unsupported opinion (weakest). Higher rungs are independently verifiable by a reviewer; lower rungs increasingly rely on trust in the person presenting them.

Defined in 2 GAGE programs, which carry 2 distinct definitions of it. The wording above is taught in Engineering Judgment and Professional Formation.

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.

Engineering Judgment and Professional Formation

A six-rung ranking of evidence strength used to decide what to lead with in a design defense: measured data from a tested prototype (strongest), a cited standard or specification, a calculation with stated assumptions, documented precedent, expert judgment or analogy, and unsupported opinion (weakest). Higher rungs are independently verifiable by a reviewer; lower rungs increasingly rely on trust in the person presenting them.

Certified AI Governance Professional (CAIGP)

The ranking of records by strength, from the system's own contemporaneous record (strongest), through derived records and contemporaneous human attestations, to after-the-fact narrative (weakest, not evidence in the strong sense). Consequential decisions should be anchored at the top of the hierarchy, not propped up by the bottom.

Where it is taught

The exact lessons this term appears in. The first module of every program is free with a free account.

Where it shows up at work

A definition is where Evidence hierarchy 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.

Collects, labels and preserves the evidence that a control operated, a decision was made, and a claim can be defended to an auditor or regulator.

One lesson on this page teach it as a primary outcome.

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.

Applied AI engineeringTechnical Evaluation

Builds production systems on foundation models: retrieval augmented generation, structured tool use, evaluation harnesses, guardrails, and cost and latency budgets.

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.

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.

“audit evidence”“record keeping”“decision log”“privacy by design”“privacy engineering”“data minimization”

Where the frameworks place it: EU AI Act Articles 11 and 12; ISO/IEC 42001 Clause 7.5; ISO/IEC 27701; GDPR Article 25.

One interview question about it

What evidence would you have ready before an auditor asks about an AI system, and how do you produce it as a byproduct of the work?

Asked of candidates for evidence collection and audit-ready documentation. 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.

Terms it appears with

Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.