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RGPD

Article 10

Data and data governance

1.   High-risk AI systems which make use of techniques involving the training of AI models with data shall be developed on the basis of training, validation and testing data sets that meet the quality criteria referred to in paragraphs 2, 3 and 4 of this Article and in Article 4a(1) whenever such data sets are used.

2.   Training, validation and testing data sets shall be subject to data governance and management practices appropriate for the intended purpose of the high-risk AI system. Those practices shall concern in particular:

(a)

the relevant design choices;

(b)

data collection processes and the origin of data, and in the case of personal data, the original purpose of the data collection;

(c)

relevant data-preparation processing operations, such as annotation, labelling, cleaning, updating, enrichment and aggregation;

(d)

the formulation of assumptions, in particular with respect to the information that the data are supposed to measure and represent;

(e)

an assessment of the availability, quantity and suitability of the data sets that are needed;

(f)

examination in view of possible biases 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;

(g)

appropriate measures to detect, prevent and mitigate possible biases identified according to point (f);

(h)

the identification of relevant data gaps or shortcomings that prevent compliance with this Regulation, and how those gaps and shortcomings can be addressed.

3.   Training, validation and testing data sets shall be relevant, sufficiently representative, and to the best extent possible, free of errors and complete in view of the intended purpose. They shall have the appropriate statistical properties, including, where applicable, as regards the persons or groups of persons in relation to whom the high-risk AI system is intended to be used. Those characteristics of the data sets may be met at the level of individual data sets or at the level of a combination thereof.

4.   Data sets shall take into account, to the extent required by the intended purpose, the characteristics or elements that are particular to the specific geographical, contextual, behavioural or functional setting within which the high-risk AI system is intended to be used.

6.   For the development of high-risk AI systems not using techniques involving the training of AI models, paragraphs 2, 3 and 4 of this Article and Article 4a(1) shall apply only to the testing data sets.

Common Questions

Frequently Asked Questions

What are the key requirements for data used in high-risk AI systems?
High-risk AI systems must use high-quality data that is relevant, sufficiently representative and, to the best extent possible, free of errors and complete in view of their intended purpose, with proper data management and clear measures to avoid and fix biases that could harm safety, violate basic rights, or lead to unfair discrimination as described under the AI Act regulation.
How should data sets be managed under the AI Act?
The AI Act states that data sets must be governed by specific practices suitable for their intended AI purpose, including clearly outlining data sources, preparation steps like labeling and cleaning, checking for biases, and addressing any data gaps or shortcomings to ensure safety, fairness, and compliance with the law.
When can special categories of personal data be used for bias detection in AI?
Special categories of personal data may be processed only exceptionally, where strictly necessary to detect and correct bias and no other data, including synthetic or anonymised data, would suffice. Article 4a's conditions, which apply in addition to the GDPR, include state-of-the-art security and privacy measures such as pseudonymisation, documented access controls, no sharing with other parties, and deletion once the bias is corrected or the retention period ends.
What steps are required to avoid bias in high-risk AI systems?

The AI Act requires AI developers to actively examine data and identify possible biases, especially those affecting people's rights, safety or leading to discrimination.

Providers must also implement effective measures to detect, prevent, or reduce such biases throughout development, ensuring the AI functions fairly and safely.

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