RM-012ÉlevéFournisseurDétectif
Évaluation des risques liés aux données d’apprentissage
Les fournisseurs doivent évaluer les risques découlant des données d’apprentissage utilisées pour développer le système d’IA, y compris les risques de biais, les défauts de qualité des données et les lacunes de représentativité pouvant affecter les performances du système vis-à-vis des personnes concernées.
Articles:Article 9(2)Article 10(2)
Exemples de preuves
- Training data risk assessment report
- Bias detection findings
- Data quality audit results
Normes
ISO 42001:2023 §6.1.2ISO/IEC 24027
Contrôles associés
- RM-001Establish Risk Management SystemArticle 9(1) requires providers to establish, implement, document, and maintain a risk management system as a continuous iterative process throughout the…
- RM-002Identify Known and Foreseeable RisksProviders must identify and analyse known and foreseeable risks that the high-risk AI system may pose to health, safety, or fundamental rights when used…
- RM-003Risk Estimation and Probability AssessmentProviders must estimate and evaluate the likelihood and severity of potential harm, taking into account the intended purpose, foreseeable misuse, and the…
- RM-004Risk Evaluation Against Acceptance CriteriaProviders must evaluate identified risks against pre-defined risk acceptance criteria and document the rationale for accepting residual risks that cannot…
- RM-005Implement Risk Treatment MeasuresProviders must adopt suitable risk management measures to address identified risks, prioritising the elimination or reduction of risk at design stage…
- RM-006Testing for Risk Management PurposesArticle 9(7) requires that high-risk AI systems are tested to identify the most appropriate risk management measures and to verify that the system…
Termes associés du règlement sur l’IA
- Risk Management SystemA continuous iterative process that must be established, implemented, documented, and maintained by providers of high-risk AI systems throughout the entire lifecycle. Must include identification and analysis of known and reasonably foreseeable risks, estimation of risks that may emerge from misuse, and evaluation of residual risks.
- Data GovernancePractices and policies applicable to training, validation, and testing data sets used for high-risk AI systems, covering the design choices, data collection, and preparation processes, and ensuring datasets are relevant, sufficiently representative, and free of errors and complete.
- Biometric DataPersonal data resulting from specific technical processing relating to the physical, physiological, or behavioural characteristics of a natural person, which allow or confirm the unique identification of that natural person, such as facial images or dactyloscopic data — a special category under GDPR Article 9.
Passez à la vitesse supérieure quand la conformité doit être
Le questionnaire gratuit fournit des signaux préliminaires. L'Évaluation Complète transforme les données réelles de vos systèmes en un dossier de décision gouverné : extraction, séparation des composants, preuves, surcouches nationales et dossier prêt à être audité.
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