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The evaluation of these technical risks is the first step of our Artificial Intelligence audit. Algorithmic risk management is the cornerstone of implementing a reliable AIMS. This report analyzes how to integrate the specific risk assessment guidelines of ISO/IEC 23894 into the overall governance framework stipulated by ISO/IEC 42001. It covers critical topics such as bias in machine learning models, data poisoning during training, and explainability (XAI). Identifying and classifying AI risk is crucial in highly regulated industries such as finance and healthcare.
Central questions answered with verifiable data from the study.
It provides particular guidelines on how to perform risk management for organizations that develop or use artificial intelligence.
While 42001 requires the implementation of a general management system with risk assessment as a pillar, 23894 provides the technical methodological mechanism to catalog the unique threats of algorithmic models.
Steps completed, sources consulted, and evidence collected during the study.
Normative and theoretical framework: ISO/IEC 42001:2023; ISO/IEC 23894:2023 (Artificial Intelligence Risk Management).
Research that extends or contrasts the findings of this study.
Help circulate evidence-based governance.
If the question is institutional and has context, we can guide you on the next steps.