Model card
Structured documentation released alongside a machine learning model that reports its intended use, known limitations, and stratified performance data, a practice established by Margaret Mitchell and colleagues at Google in 2019 and now common among major model providers as a way to make bias-relevant evidence available before deployment rather than only discoverable after.
Defined in 6 GAGE programs, which carry 12 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.
A standardized documentation artifact describing a trained model's intended use, training data at a summary level, performance characteristics, and known limitations. A model card that honestly documents a model's training data sources and any known retention or lawful-basis gaps is a natural home for the retention feasibility findings this topic's lab produces, complementing the deeper training data summary treatment at (see Topic 8.2).
Structured documentation released alongside a machine learning model that reports its intended use, known limitations, and stratified performance data, a practice established by Margaret Mitchell and colleagues at Google in 2019 and now common among major model providers as a way to make bias-relevant evidence available before deployment rather than only discoverable after.
A short, structured document that travels with a trained model and states, in plain language, what the model is, what it is for, where it works, where it fails, and how that is known. Introduced formally by Mitchell and colleagues (2019). Covers model details, intended and out-of-scope use, factors, metrics, evaluation and training data, disaggregated results, and caveats.
A short document, published alongside an AI model, that describes its intended use, known limitations, training data at a high level, and evaluation results. Format varies by developer rather than following one fixed standard, but the model card is a common developer-side transparency mechanism referenced in several current and proposed AI disclosure laws.
A documentation format (popularized by Google, 2019) giving structured information about a model's intended use, performance, training data, limitations, and ethical considerations; documentation of this kind is required for high-risk systems under the EU AI Act.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- AI Ethics: Bias, Fairness, and Accountability · Ethical and Responsible AI and Operational Governance, Certified AI Practitioner: Workplace Foundations
- Retention versus the model that memorized: deleting data a model already learned from · Consent, Purpose, and the Law of Data, Certified AI Data Governance Professional (CADGP)
- Synthetic data: when it protects, when it launders, and how to tell · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- Datasheets and the AI bill of materials: the paperwork that travels with the data · Feeding the Machines, Certified AI Data Governance Professional (CADGP)
- The license to govern: explaining to a skeptic exactly how your model fails · Build Before You Govern, Certified AI Governance Professional (CAIGP)
- Model cards and system cards for your own systems, written so an outsider could act on them · Evidence Engineering, Certified AI Governance Professional (CAIGP)
- Reading the primary source: a model card, a system card, and what they do not say · Staying Current: The Frontier Discipline, Certified AI Governance Professional (CAIGP)
- The Only Ten Things About AI That Matter in Policy · The Point Person Mandate, The AI Lobbyist: Certified AI Policy Strategist
- Building the Audit-Ready AI Program · Lab: Regulated Industries, Certified AI Transformation Professional (CATP)
- Foundation models and generalist robot policies: what to try and what is still hype · Frontier and Future Horizons, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Model 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.
3 lessons 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.
2 lessons on this page teach it as a primary outcome.
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.
2 lessons 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 Evaluation Specialistneeds it at depth · 6 open postings this week
- AI Model Validatorneeds it at depth
- Model Risk Managerneeds it at depth
- AI Governance Engineerneeds it at depth · 7 open postings this week
- Chief Technology Officer, AI engineering editionneeds it at depth
- 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: NIST AI RMF MEASURE 2; ISO/IEC 42001; EU AI Act Articles 11 and 12; ISO/IEC 42001 Clause 7.5.
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.