AR-003AltoProveedorDetective
Pruebas de robustez frente a variaciones de entrada
Los proveedores deben probar la robustez del sistema de IA frente a variaciones de entrada que puedan surgir en el despliegue real, incluidos datos incompletos, entradas ruidosas y cambios de distribución respecto al entorno de entrenamiento.
Artículos:Article 15(3)Article 15(4)
Ejemplos de evidencia
- Robustness test plan
- Input perturbation test results
- Distribution shift sensitivity analysis
Normas
ISO 42001:2023 §8.4ISO/IEC 24029-2
Controles relacionados
- 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-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,…
Términos relacionados de la Ley de IA
- 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.
Actualice cuando la conformidad deba ser
El cuestionario gratuito ofrece señales preliminares. La Evaluación Completa convierte la documentación real del sistema en un expediente de decisión auditable: extracción, separación de componentes, evidencias, normativas nacionales y dossier listo para auditoría.
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