# Recital (67)

High-quality data and access to high-quality data plays a vital role in providing structure and in ensuring the 
performance of many AI systems, especially when techniques involving the training of models are used, with a view 
to ensure that the high-risk AI system performs as intended and safely and it does not become a source of 
discrimination prohibited by Union law. High-quality data sets for training, validation and testing require the 
implementation of appropriate data governance and management practices. Data sets for training, validation and 
testing, including the labels, should be relevant, sufficiently representative, and to the best extent possible free of 
errors and complete in view of the intended purpose of the system. In order to facilitate compliance with Union data 
protection law, such as Regulation (EU) 2016/679, data governance and management practices should include, in 
the case of personal data, transparency about the original purpose of the data collection. The data sets should also 
have the appropriate statistical properties, including as regards the persons or groups of persons in relation to whom 
the high-risk AI system is intended to be used, with specific attention to the mitigation of possible biases in the data 
sets, that are likely to affect the health and safety of persons, have a negative impact on fundamental rights or lead to 
discrimination prohibited under Union law, especially where data outputs influence inputs for future operations 

(feedback loops). Biases can for example be inherent in underlying data sets, especially when historical data is being 
used, or generated when the systems are implemented in real world settings. Results provided by AI systems could be 
influenced by such inherent biases that are inclined to gradually increase and thereby perpetuate and amplify existing 
discrimination, in particular for persons belonging to certain vulnerable groups, including racial or ethnic groups. 
The requirement for the data sets to be to the best extent possible complete and free of errors should not affect the 
use of privacy-preserving techniques in the context of the development and testing of AI systems. In particular, data 
sets should take into account, to the extent required by their intended purpose, the features, characteristics or 
elements that are particular to the specific geographical, contextual, behavioural or functional setting which the AI 
system is intended to be used. The requirements related to data governance can be complied with by having recourse 
to third parties that offer certified compliance services including verification of data governance, data set integrity, 
and data training, validation and testing practices, as far as compliance with the data requirements of this Regulation 
are ensured.

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Source: Regulation (EU) 2024/1689 (Artificial Intelligence Act) as amended by Regulation (EU) 2026/1744, consolidated text of 27 July 2026 (Official Journal of the EU). This page is AI-generated legal information, not legal advice. Verify against the Official Journal.
Canonical: https://euaiactlaw.com/recital/67