RM-012HoogAanbiederDetectief
Risicobeoordeling van trainingsgegevens
Aanbieders moeten risico’s beoordelen die voortvloeien uit de trainingsgegevens waarmee het AI-systeem is ontwikkeld, waaronder bias-risico’s, gegevenskwaliteitsdefecten en hiaten in representativiteit die de prestaties van het systeem ten opzichte van betrokken personen kunnen beïnvloeden.
Artikelen:Article 9(2)Article 10(2)
Bewijs voorbeelden
- Training data risk assessment report
- Bias detection findings
- Data quality audit results
Normen
ISO 42001:2023 §6.1.2ISO/IEC 24027
Gerelateerde controles
- 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…
Gerelateerde AI-verordening-begrippen
- 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.
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De gratis vragenlijst geeft voorlopige signalen. De Volledige Beoordeling zet echte systeeminformatie om in een controleerbaar besluitdossier: extractie, componentscheiding, bewijs, nationale wetgeving en een auditklaar dossier.
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