Data & Privacy
- Data Act
- Data Governance Act
- ePrivacy Directive
- GDPR
AI & Trust
- Artificial Intelligence Act
Cybersecurity
- Cybersecurity Act
- DORA
- NIS2
Digital Services & Media
- Digital Markets Act
- Digital Services Act
- European Media Freedom Act
Artificial Intelligence Act
- Recitals
CHAPTER I
GENERAL PROVISIONSArticles 1 — 4
CHAPTER II
PROHIBITED AI PRACTICESArticles 5 — 5
CHAPTER III
HIGH-RISK AI SYSTEMSArticles 6 — 49
CHAPTER IV
TRANSPARENCY OBLIGATIONS FOR PROVIDERS AND DEPLOYERS OF CERTAIN AI SYSTEMSArticles 50 — 50
CHAPTER V
GENERAL-PURPOSE AI MODELSArticles 51 — 56
CHAPTER VI
MEASURES IN SUPPORT OF INNOVATIONArticles 57 — 63
CHAPTER VII
GOVERNANCEArticles 64 — 70
CHAPTER VIII
EU DATABASE FOR HIGH-RISK AI SYSTEMSArticles 71 — 71
CHAPTER IX
POST-MARKET MONITORING, INFORMATION SHARING AND MARKET SURVEILLANCEArticles 72 — 94
CHAPTER X
CODES OF CONDUCT AND GUIDELINESArticles 95 — 96
CHAPTER XI
DELEGATION OF POWER AND COMMITTEE PROCEDUREArticles 97 — 98
CHAPTER XII
PENALTIESArticles 99 — 101
CHAPTER XIII
FINAL PROVISIONSArticles 102 — 113
ANNEXES
Annex XIII. Criteria for the designation of general-purpose AI models with systemic risk referred to in Article 51
For the purpose of determining that a general-purpose AI model has capabilities or an impact equivalent to those set out in Article 51(1), point (a), the Commission shall take into account the following criteria:
- (a)
the number of parameters of the model;
- (b)
the quality or size of the data set, for example measured through tokens;
- (c)
the amount of computation used for training the model, measured in floating point operations or indicated by a combination of other variables such as estimated cost of training, estimated time required for the training, or estimated energy consumption for the training;
- (d)
the input and output modalities of the model, such as text to text (large language models), text to image, multi-modality, and the state of the art thresholds for determining high-impact capabilities for each modality, and the specific type of inputs and outputs (e.g. biological sequences);
- (e)
the benchmarks and evaluations of capabilities of the model, including considering the number of tasks without additional training, adaptability to learn new, distinct tasks, its level of autonomy and scalability, the tools it has access to;
- (f)
whether it has a high impact on the internal market due to its reach, which shall be presumed when it has been made available to at least 10 000 registered business users established in the Union;
- (g)
the number of registered end-users.