Validation Data

Validation data is the set of data used during the development lifecycle of an AI or machine‑learning model to provide an evaluation of a trained model and to tune non‑learnable parameters or aspects of the learning process (for example, hyperparameter selection, early stopping, or model selection). The European Union’s Artificial Intelligence Act (Regulation (EU) 2024/1689) gives a legal definition that frames validation data as data used to evaluate the trained AI system and to tune its non‑learnable parameters and learning process, in order, inter alia, to prevent underfitting or overfitting. ([eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng?utm_source=openai))

Official/legal definition (primary source): “validation data means data used for providing an evaluation of the trained AI system and for tuning its non‑learnable parameters and its learning process in order, inter alia, to prevent underfitting or overfitting.” (EU AI Act, Article 3(30)). This definition situates validation data as distinct from training data (used to fit learnable parameters) and testing data (used for an independent, confirmatory evaluation prior to market placement). ([eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng?utm_source=openai))

Jurisdictional variations: Different regulatory and standards instruments use closely aligned but not identical language—practitioners should read the differences because compliance and documentation obligations vary by regime. EU: the EU AI Act explicitly defines validation data and requires high‑quality training, validation and testing datasets (Article 10) for many high‑risk systems and requires documentation for conformity assessment. ([eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng?utm_source=openai)) United States (NIST / federal guidance): the NIST AI Risk Management Framework (AI RMF 1.0) treats validation as part of TEVV (Test, Evaluation, Verification & Validation) tasks across the lifecycle and emphasises documenting TEVV artifacts (including validation datasets and methods) in the Measure/TEVV practices rather than providing a single statutory definition. This makes NIST guidance a practical compliance reference in the U.S. even when no single federal statute defines the term. ([nist.gov](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10?utm_source=openai)) International standards: ISO/IEC 22989:2022 (AI concepts & terminology) defines validation data (development data) as data used to compare candidate models, noting it is generally disjoint from test data and can be used to tune hyperparameters; ISO places validation data in the standardized vocabulary used by other ISO AI standards. ([iso.org](https://www.iso.org/standard/74296.html?utm_source=openai)) Multilateral policy frameworks: the OECD AI Principles and the UNESCO Recommendation emphasise data quality, robustness, and lifecycle evaluation (including validation) as policy principles rather than technical definitions; these instruments shape expectations (e.g., traceability, representative samples, and bias checks) that regulators and standards bodies operationalize. ([oecd.ai](https://oecd.ai/principles/?utm_source=openai))

Context, scope and practical implications for businesses: Validation data is used during development (not usually as the final confirmatory evaluation) to select model architectures, tune hyperparameters and avoid overfitting to the training set. In regulated contexts (notably under the EU AI Act for high‑risk systems and under state laws and agency guidance in the U.S.), organisations must demonstrate that validation datasets: (a) are appropriate to the intended purpose and operational context; (b) have been curated, documented and governed to detect bias and errors; and (c) are handled to protect privacy, IP and trade secrets while still allowing auditors or notified bodies appropriate access. Practically this means producing documentation and TEVV artefacts (model development logs, validation set provenance, pre‑processing steps, metrics used, and decisions taken based on validation results) for regulators, auditors and downstream deployers. Failure to maintain adequate validation evidence can lead to non‑conformity findings under EU conformity assessments or enforcement actions where consumer protection or anti‑discrimination laws apply. ([eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng?utm_source=openai))

Key requirements / criteria (operational checklist) (common expectations across EU, ISO, and NIST guidance):

• Purpose & separation: validation data should be used for model evaluation and tuning, and should ordinarily be separate from final test data (ISO/NIST/EU guidance). ([iso.org](https://www.iso.org/standard/74296.html?utm_source=openai))
• Representativeness: validation datasets must reflect the population and operational context the model will encounter (EU AI Act Article 10 data‑quality expectations). ([eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng?utm_source=openai))
• Provenance & documentation: record source, collection method, labels, preprocessing, augmentation and any synthetic data generation used for validation. (NIST TEVV and EU technical documentation expectations). ([nist.gov](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10?utm_source=openai))
• Bias & fairness checks: validation must include subgroup analyses and fairness metrics where discrimination risks exist; remedial measures should be documented. (EU obligations for high‑risk systems; OECD/UNESCO policy expectations). ([eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng?utm_source=openai))
• Access & auditability: where conformity assessment or regulatory review is required, providers must make validation datasets or sufficiently detailed metadata and summaries available under appropriate safeguards (EU notified body access rules). ([eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng?utm_source=openai))

Examples and cross‑references: common patterns are (i) a three‑way split (training / validation / test) where validation is used to tune hyperparameters and choose models; (ii) k‑fold cross‑validation where validation folds are repeatedly formed from the training pool when data is scarce (ISO notes this practice when a strict three‑way split is infeasible); and (iii) additional external validation datasets (from different geographies or time periods) used as stronger evidence of generalisability. Cross‑references: see training data, testing data, validation dataset, and TEVV (Test, Evaluation, Verification & Validation) in NIST AI RMF and ISO vocabularies. ([iso.org](https://www.iso.org/standard/74296.html?utm_source=openai))