AR-002HochAnbieterDetektiv
Genauigkeitsmessung und -verifizierung
Anbieter müssen die Genauigkeit des KI-Systems anhand definierter Metriken mit repräsentativen Testsätzen messen und verifizieren und die Methodik, die Bedingungen und die erzielten Ergebnisse in der technischen Dokumentation dokumentieren.
Artikel:Article 15(1)Article 15(2)Annex IV §3
Nachweisbeispiele
- Accuracy measurement methodology
- Test set composition and results
- Independent verification report
Normen
ISO 42001:2023 §8.4ISO/IEC 29119
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-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-006Fallback Procedures for System FailuresProviders must design and implement fallback procedures that activate when the AI system cannot operate within its designed parameters, ensuring safe…
- 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
- Technical DocumentationThe documentation that providers of high-risk AI systems must draw up before placing the system on the market, containing all necessary information to assess compliance with the Regulation, including a general description, design specifications, training data information, risk management records, and performance metrics. Content requirements are set out in Annex IV.
- 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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