1. High-risk AI systems shall be designed and developed in such a way that they achieve an appropriate level of accuracy, robustness, and cybersecurity, and perform consistently in those respects throughout their lifecycle.
1a. To address the technical aspects of how to measure the appropriate levels of accuracy and robustness set out in paragraph 1 of this Article and any other relevant performance metrics, the Commission shall, in cooperation with relevant stakeholder and organisations such as metrology and benchmarking authorities, encourage as appropriate, the development of benchmarks and measurement methodologies.
2. The levels of accuracy and the relevant accuracy metrics of high-risk AI systems shall be declared in the accompanying instructions of use.
3. High-risk AI systems shall be as resilient as possible regarding errors, faults or inconsistencies that may occur within the system or the environment in which the system operates, in particular due to their interaction with natural persons or other systems. Technical and organisational measures shall be taken towards this regard. The robustness of high-risk AI systems may be achieved through technical redundancy solutions, which may include backup or fail-safe plans. High-risk AI systems that continue to learn after being placed on the market or put into service shall be developed in such a way to eliminate or reduce as far as possible the risk of possibly biased outputs influencing input for future operations (‘feedback loops’) are duly addressed with appropriate mitigation measures.
4. High-risk AI systems shall be resilient as regards to attempts by unauthorised third parties to alter their use, outputs or performance by exploiting the system vulnerabilities. The technical solutions aimed at ensuring the cybersecurity of high-risk AI systems shall be appropriate to the relevant circumstances and the risks. The technical solutions to address AI specific vulnerabilities shall include, where appropriate, measures to prevent, detect, respond to, resolve and control for attacks trying to manipulate the training dataset (‘data poisoning’), or pre-trained components used in training (‘model poisoning’) , inputs designed to cause the model to make a mistake (‘adversarial examples’ or ‘model evasion’), confidentiality attacks or model flaws.