Model card / system card
A primary-source document from a model's maker describing what the model is, how it was evaluated, and its known limits. The place you verify a capability claim rather than trusting a headline. Different labs use different formats and different levels of detail, and a card can lag a live deployment, so a claim about current behavior sometimes needs a cross-check against the provider's changelog or release notes, not the card alone. (see Topic 12.1)
Defined in 2 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Governance Professional (CAIGP).
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 primary-source document from a model's maker describing what the model is, how it was evaluated, and its known limits. The place you verify a capability claim rather than trusting a headline. Different labs use different formats and different levels of detail, and a card can lag a live deployment, so a claim about current behavior sometimes needs a cross-check against the provider's changelog or release notes, not the card alone. (see Topic 12.1)
A structured document published by an AI developer describing a specific model's intended use, architecture summary, known limitations, and evaluation results.
A document a developer publishes alongside an AI model or system describing its intended use, capabilities, evaluations, and limitations. A primary source that is read both for what it claims and for what it conspicuously does not address (such as misuse resistance or evaluation conditions).
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The weekly frontier hour: a sustainable practice for staying current for a career · Staying Current: The Frontier Discipline, Certified AI Governance Professional (CAIGP)
- Separating signal from theater: which AI news changes your decisions and which is noise · Staying Current: The Frontier Discipline, Certified AI Governance Professional (CAIGP)
- How AI Actually Works Under the Hood: Architectures and Limits for Policy People · Technical Credibility Deep Dive, The AI Lobbyist: Certified AI Policy Strategist
Where it shows up at work
A definition is where Model card / system card 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.
Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.
One lesson on this page teach it as a primary outcome.
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.
Recognizes hallucination, drift, skew, brittleness and biased outcomes, and knows how each one enters a system.
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 Model Validatorneeds it at depth
- AI Evaluation Specialistneeds it at depth · 6 open postings this week
- Chief Technology Officer, AI engineering editionneeds it at depth
- AI Regulatory Counselneeds working knowledge · 14 open postings this week
- AI Product Manager, responsible product editionneeds working knowledge · 3 open postings this week
- AI Security Architectneeds it at depth · 8 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: EU AI Act Articles 13 and 50; NIST AI RMF trustworthiness characteristics; NIST AI RMF MEASURE 2; ISO/IEC 42001.
One interview question about it
Explain how a large language model produces an answer, at the depth a governance decision needs and no deeper.
Asked of candidates for how models work, at a governance depth. 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.
- Data analysis, stewardship and business intelligence carries it as a claim to back up: SQL and analytical fluency make the technical layer approachable.
- Model risk, credit risk and quantitative analysis carries it as work you can show: Quantitative fluency is demonstrated in your work.
- A computer science or software engineering degree carries it as work you can show: You have trained or integrated a model in coursework or a project.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.