Outlier Detection
Techniques to identify data points or model predictions that deviate significantly from expected patterns, triggering review or mitigation actions.
Methods include statistical thresholds (z-score), distance-based algorithms (k-NN outlier score), or model-based residual analysis. Outlier detection flags anomalies in input data (e.g., corrupt sensor readings) or outputs (e.g., improbable predictions). Governance defines acceptable anomaly rates, configures automated alerts or safe-mode fallbacks, and establishes workflows for investigation and remediation of flagged items.
A manufacturing-quality AI monitors sensor streams for equipment. It uses a random-forest anomaly detector to flag any temperature reading beyond three standard deviations. When outliers occur, the system alerts operators, pauses the assembly line, and logs the event for root-cause analysis - preventing defective product batches.
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