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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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