Description
Regular measurement and reporting of AI system performance against defined metrics including accuracy, fairness, and latency.
Implementation Guidance
Performance Metrics Framework
Define a comprehensive metrics framework for each AI system: primary performance metrics (accuracy, AUC, RMSE as appropriate), fairness metrics (equalized odds, demographic parity across protected groups), operational metrics (latency, throughput, availability), and business metrics (decision quality, user satisfaction).
Measurement Cadence
Establish measurement schedules: real-time monitoring for operational metrics, daily or weekly performance metric computation, monthly fairness metric deep-dives, and quarterly comprehensive performance reviews reported to governance.
Performance Reporting
Generate standardized performance reports for stakeholders: executive dashboards showing business impact, technical reports with statistical detail for data science teams, fairness reports for ethics committee, and trend analysis showing performance over time.
Evidence Requirements
- Performance metrics framework per AI system
- Automated performance measurement configurations
- Performance reports and trend analysis
- Fairness metric reports across protected groups