A practical topic hub for understanding EU AI Act concepts, regulated roles, market access duties, and operational compliance controls.
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Terms
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Comparisons
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Questions
The EU AI Act topic connects legal definitions to practical compliance work: identifying roles, classifying systems, documenting intended purpose, and preparing for monitoring duties. It is designed for readers who need to translate legal vocabulary into product and governance decisions.
Most AI compliance projects fail when teams cannot map system ownership, lifecycle responsibilities, and evidence requirements. A shared vocabulary reduces confusion between providers, deployers, importers, distributors, and downstream providers.
Useful for early AI inventory reviews, AI Act readiness assessments, GPAI governance, conformity planning, incident handling, and internal training.
Core actors and scope concepts used to decide who must do what under the AI Act.
Terms that connect AI systems to documentation, assessment, CE marking, and post-market duties.
Institutional and general-purpose AI concepts relevant to supervision, systemic risk, and high-impact capabilities.
A side-by-side comparison of Provider and Deployer. Understand who develops or places an AI system on the market and who uses it under their authority.
A side-by-side comparison of Importer and Distributor. Understand the difference between placing a non-EU branded AI system on the EU market and making an AI system available in the EU supply chain.
A side-by-side comparison of Provider and Downstream Provider. Understand the difference between the general provider role and a provider that integrates an AI model into its own system or offering.
A side-by-side comparison of Conformity Assessment and CE Marking. Understand how the compliance evaluation process differs from the mark signaling that applicable EU requirements have been met.
A side-by-side comparison of General-Purpose AI and Foundation Model. Understand how one describes broad adaptability and the other describes a large pretrained base model supporting downstream tasks.
A side-by-side comparison of General-Purpose AI and Frontier AI. Understand the difference between broadly adaptable AI and highly capable general-purpose systems associated with novel safety and policy risks.
A side-by-side comparison of Placing on the Market and Putting into Service. Understand how first EU market availability differs from first operational use for an intended purpose.
A side-by-side comparison of Intended Purpose and Reasonably Foreseeable Misuse. Understand how declared system use differs from plausible off-purpose use and why both matter for AI risk classification and compliance evidence.
Placing on the market means the first making available of an AI system or general-purpose AI model on the EU market. The term is important because certain regulatory obligations attach at or before this point.
Intended purpose is the use for which an AI system is designed, marketed, or deployed by its provider. It defines the expected context, users, functions, outputs, and limitations, and is central to risk classification, compliance, and accountability.
Reasonably foreseeable misuse is the use of an AI system outside its intended purpose in a way that can still be anticipated from human behavior or interaction with other systems.
CE marking is a conformity mark indicating that a product or AI system meets applicable requirements under relevant European Union law. For AI systems, it signals that the provider has assessed compliance with the legal obligations that apply to the system.
General-Purpose AI (GPAI) refers to AI models or systems that can be adapted to a wide range of tasks and applications. Foundation models are a common example, in contrast to narrow AI systems designed for a specific task.
Systemic risk is a risk associated with high-impact capabilities of general-purpose AI models that can produce significant effects across markets, society, public health, safety, security, or fundamental rights.
The AI Office is the European Commission function established to support implementation, monitoring, and supervision of AI systems, general-purpose AI models, and AI governance under the EU AI framework.
A serious incident is an AI-system incident or malfunction that directly or indirectly causes severe harm, such as death, serious health harm, critical infrastructure disruption, fundamental-rights infringement, or serious property or environmental damage.