AI System KPIs
Comprehensive KPI table for monitoring AI system performance
| Category | KPI | Description | Target | Measurement Method | Frequency |
|---|---|---|---|---|---|
| Performance Metrics | Accuracy Score | Overall correctness of model predictions | >95% | Test set evaluation | Weekly |
| Precision-Recall Balance | Trade-off between precision and false positives | F1 Score >0.90 | Precision-recall curve analysis | Weekly | |
| Robustness Score | Performance stability under adversarial inputs | <5% degradation | Garak vulnerability testing | Monthly | |
| Drift Detection Rate | Identification of performance decay over time | <2% monthly drift | Distribution monitoring | Daily | |
| Recovery Time | Time to restore performance after drift | <24 hours | System logs | Per incident | |
| Fairness Metrics | Demographic Parity Ratio | Equality of positive prediction rates across groups | 0.90-1.10 | Between-group comparison | Monthly |
| Equal Opportunity Ratio | Equality of true positive rates across groups | 0.90-1.10 | Conditional probability analysis | Monthly | |
| Disparate Impact Score | Relative harm/benefit ratio across groups | <10% difference | Impact assessment framework | Quarterly | |
| Representation Balance | Data distribution across protected attributes | <5% deviation | Dataset analysis | Quarterly | |
| Bias Mitigation Success | Effectiveness of fairness interventions | >80% improvement | Pre/post intervention comparison | Per intervention | |
| Operational Metrics | Incident Frequency | Number of AI system failures or issues | <2 per month | Incident tracking system | Monthly |
| Mean Time to Detection | Average time to identify an issue | <4 hours | System logs | Per incident | |
| Resolution Time | Average time to resolve identified issues | <24 hours | Ticket system | Monthly | |
| SLA Compliance Rate | Adherence to service level agreements | >99% | Automated monitoring | Weekly | |
| Automation Efficiency | Ratio of automated to manual interventions | >90% automation | Process logs | Monthly | |
| User Impact Metrics | User Satisfaction Score | Explicit feedback from users | >4.5/5 | In-app surveys | Continuous |
| Trust Score | User confidence in AI recommendations | >85% | Periodic surveys | Quarterly | |
| Value Realization | Business outcomes attributed to AI system | ROI >3x | Value tracking framework | Quarterly | |
| Adoption Rate | Percentage of eligible users actively using system | >80% | Usage analytics | Monthly | |
| Feature Utilization | Distribution of feature usage across system | Even distribution | Feature analytics | Monthly |
Implementation Notes
- Create dashboards that visualize these metrics with appropriate thresholds and alerts
- Implement automated collection for metrics where possible using your Giskard monitoring
- Establish baselines during initial deployment before setting hard targets
- Review and adjust targets quarterly based on evolving business needs and technical capabilities
- Correlate metrics across categories to identify systemic issues (e.g., fairness problems affecting user satisfaction)