AR-006HochAnbieterKorrektiv
Rückfallverfahren bei Systemausfällen
Anbieter müssen Rückfallverfahren entwerfen und umsetzen, die aktiviert werden, wenn das KI-System nicht innerhalb seiner Auslegungsparameter betrieben werden kann, um eine sichere Leistungsminderung und Benachrichtigung der menschlichen Aufseher zu gewährleisten.
Artikel:Article 15(3)Article 14
Nachweisbeispiele
- Fallback procedure specification
- Graceful degradation design description
- Fallback activation test records
Normen
ISO 42001:2023 §8.4
Verwandte Kontrollen
- AR-001Accuracy Level Definition and CommitmentArticle 15(1) requires that high-risk AI systems are designed and developed to achieve an appropriate level of accuracy, robustness, and cybersecurity,…
- AR-002Accuracy Measurement and VerificationProviders must measure and verify the accuracy of the AI system against defined metrics using representative test sets, documenting the methodology,…
- AR-003Robustness Testing Against Input VariationsProviders must test the AI system's robustness against input variations that may arise in real-world deployment, including incomplete data, noisy inputs,…
- AR-004Adversarial Robustness TestingWhere relevant to the AI system's use case, providers must test robustness against adversarial inputs designed to cause misclassification, data poisoning…
- AR-005Cybersecurity Measures for AI SystemsArticle 15(5) requires that high-risk AI systems are resilient against unauthorised third-party attempts to alter their outputs, and providers must…
- AR-007Input Validation and Data Quality ChecksProviders must implement input validation mechanisms that verify the quality, format, and plausibility of data before it is processed by the AI system,…
Verwandte KI-VO-Begriffe
- Human OversightMeasures built into high-risk AI systems enabling natural persons to understand, monitor, and — where necessary — override or shut down the system. Must be proportionate to the risks and must ensure that deployers can intervene in the system's output before it takes effect.
- AccuracyThe requirement that high-risk AI systems achieve an appropriate level of accuracy in relation to their intended purpose, as specified in the technical documentation. Providers must declare the level of accuracy in the instructions for use.
- RobustnessThe ability of a high-risk AI system to maintain its level of performance under adverse conditions — including technical limitations, adversarial inputs, errors, or unexpected situations — or within foreseeable operating conditions outside the intended purpose.
- CybersecurityThe requirement that high-risk AI systems are resilient against attempts by third parties to alter their use, behaviour, or performance in ways that could result in risks to health, safety, or fundamental rights, including protection against data poisoning, adversarial examples, and model evasion attacks.
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