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Question 1 of 30
A medical AI system trained to identify early signs of a rare neurological disorder in patient scans has been deployed in a clinical setting. After several months of operation, an internal audit reveals a subtle but consistent decrease in the system\'s sensitivity for detecting the disorder in patients from a specific demographic group, a group that was underrepresented in the initial training data. This phenomenon is not immediately apparent from standard performance dashboards, which primarily track overall accuracy. According to the principles outlined in ISO/IEC 23053:2022 for AI system lifecycle management, what is the most appropriate immediate action to address this situation to ensure continued trustworthiness and compliance?
Initiate a targeted data augmentation strategy for the underrepresented demographic group and re-evaluate the model's performance on this specific cohort before broader recalibration.
Immediately halt all operations of the AI system and revert to manual diagnostic procedures until a complete system overhaul can be performed.
Adjust the system's confidence threshold downwards for all predictions to compensate for the observed decrease in sensitivity, without further investigation.
Focus solely on improving the overall system accuracy metrics by retraining on a balanced dataset, disregarding the specific demographic performance disparity.

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