The Articles of the EU Artificial Intelligence Act (25.11.2022)

Preamble 41 to 50, Artificial Intelligence Act (Proposal 25.11.2022)

(41) The fact that an AI system is classified as high risk under this Regulation should not be interpreted as indicating that the use of the system is lawful under other acts of Union law or under national law compatible with Union law, such as on the protection of personal data, on the use of polygraphs and similar tools or other systems to detect the emotional state of natural persons.

Any such use should continue to occur solely in accordance with the applicable requirements resulting from the Charter and from the applicable acts of secondary Union law and national law. This Regulation should not be understood as providing for the legal ground for processing of personal data, including special categories of personal data, where relevant, unless it is specifically provided for otherwise in this Regulation.

(42) To mitigate the risks from high-risk AI systems placed or otherwise put into service on the Union market, certain mandatory requirements should apply, taking into account the intended purpose of the use of the system and according to the risk management system to be established by the provider.

In particular, the risk management system should consist of a continuous iterative process planned and run throughout the entire lifecycle of a high-risk AI system. This process should ensure that the provider identifies and analyses the risks to the health, safety and fundamental rights of the persons who may be affected by the system in light of its intended purpose, including the possible risks arising from the interaction between the AI system and the environment within which it operates, and accordingly adopts suitable risk management measures in the light of state of the art.

(43) Requirements should apply to high-risk AI systems as regards the quality of data sets used, technical documentation and record-keeping, transparency and the provision of information to users, human oversight, and robustness, accuracy and cybersecurity. Those requirements are necessary to effectively mitigate the risks for health, safety and fundamental rights, as applicable in the light of the intended purpose of the system, and no other less trade restrictive measures are reasonably available, thus avoiding unjustified restrictions to trade.

(44) High data quality is essential for 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 the source of discrimination prohibited by Union law. High quality training, validation and testing data sets require the implementation of appropriate data governance and management practices. Training, validation and testing data sets should be sufficiently relevant, representative and have the appropriate statistical properties, including as regards the persons or groups of persons on which the high-risk AI system is intended to be used.

These datasets should also be as free of errors and complete as possible in view of the intended purpose of the AI system, taking into account, in a proportionate manner, technical feasibility and state of the art, the availability of data and the implementation of appropriate risk management measures so that possible shortcomings of the datasets are duly addressed. The requirement for the datasets to be complete and free of errors should not affect the use of privacy-preserving techniques in the context of the the development and testing of AI systems.

Training, validation and testing 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, behavioural or functional setting or context within which the AI system is intended to be used. In order to protect the right of others from the discrimination that might result from the bias in AI systems, the providers should be able to process also special categories of personal data, as a matter of substantial public interest within the meaning of Article 9(2)(g) of Regulation (EU) 2016/679 and Article 10(2)g) of Regulation (EU) 2018/1725, in order to ensure the bias monitoring, detection and correction in relation to high-risk AI systems.

(44a) When applying the principles referred to in Article 5(1)(c) of Regulation 2016/679 and Article 4(1)(c) of Regulation 2018/1725, in particular the principle of data minimisation, in regard to training, validation and testing data sets under this Regulation, due regard should be had to the full life cycle of the AI system.

(45) For the development of high-risk AI systems, certain actors, such as providers, notified bodies and other relevant entities, such as digital innovation hubs, testing experimentation facilities and researchers, should be able to access and use high quality datasets within their respective fields of activities which are related to this Regulation. European common data spaces established by the Commission and the facilitation of data sharing between businesses and with government in the public interest will be instrumental to provide trustful, accountable and non-discriminatory access to high quality data for the training, validation and testing of AI systems.

For example, in health, the European health data space will facilitate non-discriminatory access to health data and the training of artificial intelligence algorithms on those datasets, in a privacy-preserving, secure, timely, transparent and trustworthy manner, and with an appropriate institutional governance. Relevant competent authorities, including sectoral ones, providing or supporting the access to data may also support the provision of high-quality data for the training, validation and testing of AI systems.

(46) Having information on how high-risk AI systems have been developed and how they perform throughout their lifecycle is essential to verify compliance with the requirements under this Regulation. This requires keeping records and the availability of a technical documentation, containing information which is necessary to assess the compliance of the AI system with the relevant requirements.

Such information should include the general characteristics, capabilities and limitations of the system, algorithms, data, training, testing and validation processes used as well as documentation on the relevant risk management system. The technical documentation should be kept up to date. Furthermore, providers or users should keep logs automatically generated by the high-risk AI system, including for instance output data, start date and time etc., to the extent that such a system and the related logs are under their control, for a period that is appropriate to enable them to fulfil their obligations.

(47) To address the opacity that may make certain AI systems incomprehensible to or too complex for natural persons, a certain degree of transparency should be required for highrisk AI systems. Users should be able to interpret the system output and use it appropriately. High-risk AI systems should therefore be accompanied by relevant documentation and instructions of use and include concise and clear information, including in relation to possible risks to fundamental rights and discrimination of the persons who may be affected by the system in light of its intended purpose, where appropriate. To facilitate the understanding of the instructions of use by users, they should contain illustrative examples, as appropriate.

(48) High-risk AI systems should be designed and developed in such a way that natural persons can oversee their functioning. For this purpose, appropriate human oversight measures should be identified by the provider of the system before its placing on the market or putting into service. In particular, where appropriate, such measures should guarantee that the system is subject to in-built operational constraints that cannot be overridden by the system itself and is responsive to the human operator, and that the natural persons to whom human oversight has been assigned have the necessary competence, training and authority to carry out that role.

Considering the significant consequences for persons in case of incorrect matches by certain biometric identification systems, it is appropriate to provide for an enhanced human oversight requirement for those systems so that no action or decision may be taken by the user on the basis of the identification resulting from the system unless this has been separately verified and confirmed by at least two natural persons. Those persons could be from one or more entities and include the person operating or using the system. This requirement should not pose unnecessary burden or delays and it could be sufficient that the separate verifications by the different persons are automatically recorded in the logs generated by the system.

(49) High-risk AI systems should perform consistently throughout their lifecycle and meet an appropriate level of accuracy, robustness and cybersecurity in accordance with the generally acknowledged state of the art. The level of accuracy and accuracy metrics should be communicated to the users.

(50) The technical robustness is a key requirement for high-risk AI systems. They should be resilient in relation to harmful or otherwise undesirable behaviour that may result from limitations within the systems or the environment in which the systems operate (e.g. errors, faults, inconsistencies, unexpected situations). High-risk AI systems should therefore be designed and developed with appropriate technical solutions to prevent or minimize that harmful or otherwise undesirable behaviour, such as for instance mechanisms enabling the system to safely interrupt its operation (fail-safe plans) in the presence of certain anomalies or when operation takes place outside certain predetermined boundaries. Failure to protect against these risks could lead to safety impacts or negatively affect the fundamental rights, for example due to erroneous decisions or wrong or biased outputs generated by the AI system.

Important note: This is not the final text of the Artificial Intelligence Act. This is the text of the proposal from the Council of the European Union (25.11.2022).

The Articles of the EU Artificial Intelligence Act, proposal from the Council of the European Union (25.11.2022):