Intended use
The specific purposes and contexts for which a provider has validated and designed an AI system, as specified in the Article 13 instructions for use and the Article 11 technical documentation. Deployment outside the intended use shifts liability toward the deployer under Article 25 of the AI Act and reduces the provider's PLD exposure.
Defined in 2 GAGE programs, which carry 5 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.
The specific purposes and contexts for which a provider has validated and designed an AI system, as specified in the Article 13 instructions for use and the Article 11 technical documentation. Deployment outside the intended use shifts liability toward the deployer under Article 25 of the AI Act and reduces the provider's PLD exposure.
The specific, testable statement of what a model or system is for, who its users are, and in what setting. Narrow enough to evaluate and to write an out-of-scope statement against; a broad intended-use statement ("general classification") is a warning sign because nothing can be validated or excluded against it.
A short statement of what the model is for, included in the failure account so a reader can distinguish an in-scope failure (a real problem) from an out-of-scope one (a use the system was never meant to support).
What an organization now wants to do with data it already holds. Consent archaeology is the comparison of the intended use against the collection purpose.
The section of a card stating the uses the vendor validated the model or system for. The bound your actual use must be checked against.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- The license to govern: explaining to a skeptic exactly how your model fails · Build Before You Govern, AI Governance: Applied Mastery
- Consent archaeology: what this data was collected for versus what you want to do · Data Reality, AI Governance: Applied Mastery
- Model cards and system cards for your own systems, written so an outsider could act on them · Evidence Engineering, AI Governance: Applied Mastery
- Reading the primary source: a model card, a system card, and what they do not say · Staying Current: The Frontier Discipline, AI Governance: Applied Mastery
- The AI Liability Landscape: Proving Fault When AI Causes Harm · Regulatory Interplay and Liability, EU AI Act Implementation Expert
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.