RM-003KritiekAanbiederDetectief
Risicoschatting en waarschijnlijkheidsbeoordeling
Aanbieders moeten de waarschijnlijkheid en ernst van potentiële schade schatten en evalueren, rekening houdend met het beoogde doeleinde, voorzienbaar misbruik en de kwetsbaarheid van betrokken natuurlijke personen.
Artikelen:Article 9(2)(b)Article 9(5)
Bewijs voorbeelden
- Risk probability matrix
- Severity assessment worksheet
- Harm estimation methodology
Normen
ISO 42001:2023 §6.1.2ISO/IEC 23894
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-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…
- RM-007Residual Risk Documentation and DisclosureProviders must document residual risks that users need to be informed of and include relevant information in the system instructions for use, enabling…
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.
- 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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