(75) 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). Therefore, technical and organisational measures should be taken to ensure robustness of high-risk AI systems, for example by designing and developing appropriate technical solutions to prevent or minimise harmful or otherwise undesirable behaviour. Those technical solution may include 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 rightsfundamental rightsIncludes human dignity, right to life, prohibition of torture, protection of personal datapersonal dataAny information relating to an identified or identifiable natural person ('data subjectsubjectfor the purpose of real-world testing, means a natural person who participates in testing in real-world conditionsArticle 3(58)data subjectsubjectfor the purpose of real-world testing, means a natural person who participates in testing in real-world conditionsArticle 3(58)An identified or identifiable natural person to whom personal data relateGDPR Art. 4(1)'). Includes name, ID number, location data, online identifiers, or factors specific to physical, physiological, genetic, mental, economic, cultural or social identityGDPR Art. 4(1), freedom of expression, non-discrimination, equality between women and men, rights of the child, right to an effective remedy and fair trialCharter of Fundamental Rights Art. 1–54, for example due to erroneous decisions or wrong or biased outputs generated by the AI systemAI systemmeans a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environmentsArticle 3(1).