Monitoring AI system data quality is crucial to ensuring that the AI system sustains the desired level of operational performance. Data quality monitoring must be systematic and metrics-based to achieve consistency over time.
The AI System Owner should ensure that the organization defines, documents, and entrenches workflows and technical interfaces to facilitate the monitoring of data quality. In particular, the AI System Owner should identify anomalous data entries and data drift. including
1) automated or manual production and reporting of data quality indicators, alarm thresholds, and
2) workflows that allocate monitoring responsibilities.
3) workflows to address issues detected during regular monitoring.
The Algorithm Owner should ensure that the data quality design process aligns with the organization’s values and risk tolerance.