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Rebuttable presumption

An assumption of fact or law that a court accepts as true unless the opposing party produces sufficient evidence to overcome it. Under the Revised PLD, defect and causation can be presumed in specified circumstances, shifting the burden to the defendant to rebut. (Revised PLD, Articles 10(2), 10(3), 10(4).)

Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in EU AI Act Implementation Expert.

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.

EU AI Act Implementation Expert

An assumption of fact or law that a court accepts as true unless the opposing party produces sufficient evidence to overcome it. Under the Revised PLD, defect and causation can be presumed in specified circumstances, shifting the burden to the defendant to rebut. (Revised PLD, Articles 10(2), 10(3), 10(4).)

Certified AI Governance Professional (CAIGP)

A legal presumption that holds unless contrary evidence overturns it. The Article 40 presumption of conformity is rebuttable: conforming to a harmonised standard makes you presumed compliant, but a regulator can still show, on the facts, that you did not actually meet the requirement.

The AI Lobbyist: Certified AI Policy Strategist

a legal assumption that a court will accept as true unless the opposing party presents evidence disproving it.

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 Rebuttable presumption 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.

Decides what a person affected by an AI decision must be told, how an output can be explained, and how they can challenge it.

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

Assures AI systems that can cause physical harm: hazard analysis, safety cases, runtime monitors, and the standards evidence a regulator or a court will ask for.

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

Knows how a rule becomes a rule, drafts comments for the record that get read, and prepares testimony and briefings for hearings.

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.

“disclosure to users”“model cards”“system cards”“robot safety”“functional safety”“safety case”

Where the frameworks place it: EU AI Act Articles 13 and 50; NIST AI RMF trustworthiness characteristics; EU AI Act; EU Machinery Regulation.

One interview question about it

A customer asks why the model decided against them. What can you explain, what can you not, and how can they contest it?

Asked of candidates for explainability, transparency and contestability. 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.