AI Glossary
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A document by which the provider declares that a high-risk AI system meets the requirements of the AI Act and other relevant regulations.
The author's rights to use a work and to decide on its commercial exploitation, which can be licensed or transferred.
Running models directly on end devices (phone, sensor) without the need to send data to the cloud, which can improve both privacy and responsiveness.
Interpretive guidance from the European Data Protection Board on when a model can be considered anonymous and how to assess legitimate interest during training.
The European framework for electronic identification and trust services, relevant for identity verification, electronic signatures, and trustworthy proof of origin, including in AI environments.
A platform for voice synthesis and voice cloning, associated with questions of voice rights protection and fraud using imitated voices.
A numerical representation of the meaning of a word, text or image in a multidimensional space that makes it possible to compare the similarity of content; the basis of vector search.
Capabilities or behaviors that appear unexpectedly in larger models and were not directly programmed or intended during training.
An AI system designed to infer a person's emotions or psychological states from their expressions.
A particularly sensitive use of emotion-inference systems, which the AI Act prohibits in the workplace and education settings, subject to exceptions.
The part of a model that converts input (e.g. text) into an internal representation suitable for further processing, such as classification or search.
The process of converting information into a form readable only by authorized persons.
The energy and water consumption associated with training and operating large models and running data centers; a growing topic in regulation and corporate responsibility.
A predefined procedure for how and to whom to report identified AI risks or incidents so that they reach the appropriate level of decision-making.
An office established within the European Commission that oversees in particular general-purpose AI models, coordinates the application of the AI Act and prepares guidance on it.
A coordination body composed of representatives of the Member States that supports the consistent application of the AI Act and cooperation in its implementation.
A dataset set aside to verify a model's performance and reliability, which is not used during its training so that the evaluation is objective.
The hypothetical risk that very advanced AI systems could cause catastrophic impacts on humanity; a subject of both expert and political debate.
The ability to understand and clearly explain how an AI system arrived at a result or decision.
An approach that makes it possible to explain the functioning and results of an AI system so that a human can understand them.
A feature of a legal framework whereby it applies also to entities outside a given territory if their systems affect persons or the market in that jurisdiction; typical of the European AI Act.