SHORTS: Design KPIs for M&E that detect AI bias over time
Design KPIs for M&E systems to detect AI bias over time by tracking fairness, drift, and equity metrics across deployment cycles, ensuring evaluations remain reliable for program impacts. These KPIs integrate longitudinal monitoring with standard M&E indicators like logframe outputs.sparkco+1
Fairness Disparity Metrics
Monitor Disparate Impact Ratio (DIR): favorable outcome rate (protected group A) / favorable outcome rate (protected group B), targeting <0.8 or >1.25 thresholds quarterly. Track Equal Opportunity Difference (EOD): |True Positive Rate A – True Positive Rate B|, alerting if >0.1 over cycles, capturing bias growth in beneficiary predictions [sparkco].
Drift and Amplification KPIs
Bias Amplification Delta: (Cycle_t fairness metric – Cycle_0 metric) / Cycle_0 * 100%, flagging >5% rises in Gini or predictability scores for attributes like region/gender. Population Drift Score: Kolmogorov-Smirnov distance between deployment data distributions vs. baseline, >0.05 triggers retrain.[relyance]
Operational Equity Indicators
Group Error Rate Parity (GERP): |Error rate A – Error rate B| <0.05, logged per evaluation dashboard use. Stakeholder Override Rate: % of AI recommendations overridden by humans, >15% signals undetected bias or over-reliance [thesai].
Monitoring Cadence
| KPI | Frequency | Threshold | Action |
|---|---|---|---|
| DIR | Monthly | <0.8/>1.25 | Audit data pipeline [sparkco] |
| Bias Amplification Delta | Quarterly | >5% | Retrain model [ceur-ws] |
| EOD | Per deployment | >0.1 | Stakeholder review |
| GERP | Real-time | >0.05 | Pause predictions [sparkco] |
Automate via dashboards with policy-as-code alerts for continuous observability in M&E workflows.[relyance]
