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Removed on May 8th 2024 based on the version and article numbering in the EU Parliament's 'Corrigendum' version dated April 19th 2024.

[Previous version]

Removed on April 10th 2024 based on the version and article numbering approved by the EU Parliament on March 13th 2024.

Updated on Feb 6th 2024 based on the version endorsed by the Coreper I on Feb 2nd

While the risk-based approach is the basis for a proportionate and effective set of binding rules, it is important to recall the 2019 Ethics Guidelines for Trustworthy AI developed by the independent High-Level Expert Group on AI (HLEG) appointed by the Commission. In those Guidelines the HLEG developed seven non-binding ethical principles for AI which should help ensure that AI is trustworthy and ethically sound. The seven principles include: human agency and oversight; technical robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental well-being and accountability. Without prejudice to the legally binding requirements of this Regulation and any other applicable Union law, these Guidelines contribute to the design of a coherent, trustworthy and human-centric Artificial Intelligence, in line with the Charter and with the values on which the Union is founded. According to the Guidelines of HLEG, human agency and oversight means that AI systems are developed and used as a tool that serves people, respects human dignity and personal autonomy, and that is functioning in a way that can be appropriately controlled and overseen by humans. Technical robustness and safety means that AI systems are developed and used in a way that allows robustness in case of problems and resilience against attempts to alter the use or performance of the AI system so as to allow unlawful use by third parties, and minimise unintended harm. Privacy and data governance means that AI systems are developed and used in compliance with existing privacy and data protection rules, while processing data that meets high standards in terms of quality and integrity.

Transparency means that AI systems are developed and used in a way that allows appropriate traceability and explainability, while making humans aware that they communicate or interact with an AI system, as well as duly informing deployers of the capabilities and limitations of that AI system and affected persons about their rights.

Diversity, non-discrimination and fairness means that AI systems are developed and used in a way that includes diverse actors and promotes equal access, gender equality and cultural diversity, while avoiding discriminatory impacts and unfair biases that are prohibited by Union or national law. Social and environmental well-being means that AI systems are developed and used in a sustainable and environmentally friendly manner as well as in a way to benefit all human beings, while monitoring and assessing the long-term impacts on the individual, society and democracy. The application of these principles should be translated, when possible, in the design and use of AI models. They should in any case serve as a basis for the drafting of codes of conduct under this Regulation. All stakeholders, including industry, academia, civil society and standardisation organisations, are encouraged to take into account as appropriate the ethical principles for the development of voluntary best practices and standards.

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