AI Act Documents:
Recital 114
(114) The providers of general-purpose AI models presenting systemic risks should be subjectsubjectfor the purpose of real-world testing, means a natural person who participates in testing in real-world conditionsArticle 3(58), in addition to the obligations provided for providers of general-purpose AI models, to obligations aimed at identifying and mitigating those risks and ensuring an adequate level of cybersecurity protection, regardless of whether it is provided as a standalone model or embedded in an AI systemAI systemmeans a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environmentsArticle 3(1) or a product. To achieve those objectives, this Regulation should require providers to perform the necessary model evaluations, in particular prior to its first placing on the marketplacing on the marketmeans the first making available of an AI systemAI systemmeans a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environmentsArticle 3(1) or a general-purpose AI model on the Union marketArticle 3(9), including conducting and documenting adversarial testing of models, also, as appropriate, through internal or independent external testing. In addition, providers of general-purpose AI models with systemic risks should continuously assess and mitigate systemic risks, including for example by putting in place risk-management policies, such as accountability and governance processes, implementing post-market monitoring, taking appropriate measures along the entire model’s lifecycle and cooperating with relevant actors along the AI value chain.