The temporal and geographic scope of a dataset directly affects its suitability for training a high-risk AI system deployed in the EU. Article 10(3) requires datasets to be “relevant, sufficiently representative, and to the best extent possible, free of errors and complete.” Temporal and geographic coverage are core dimensions of representativeness.

Temporal coverage records the start and end dates of the data collection period. The Technical SME assesses whether the period is sufficient to capture seasonal, cyclical, and trend variations relevant to the system’s intended purpose. A model trained on twelve months of data may miss multi-year patterns; a model trained during a period of unusual economic conditions (a pandemic, a financial crisis) may not generalise to normal conditions. The assessment is documented with the conclusion and supporting rationale.

Geographic scope records the jurisdictions, regions, or member states from which the data originates. For systems intended for deployment across the EU/EEA, the data should reflect the deployment population’s geographic diversity. A credit scoring model trained predominantly on UK financial behaviour data may not generalise to markets in other member states with different consumer protection frameworks and lending practices. Geographic gaps are documented as known limitations.

Where temporal or geographic coverage is insufficient, the AISDP records the compensating controls: synthetic data augmentation, transfer learning from related domains, stratified sampling to ensure small subgroups are represented in validation and test sets, or deployment restrictions limiting the system’s use to populations the data adequately represents.

Key outputs

• Temporal coverage assessment per dataset
• Geographic scope assessment per dataset
• Gap documentation and compensating controls