SHORTS: Identify measurable outcomes used to detect AI bias in healthcare M&E
Measurable outcomes for detecting AI bias in healthcare M&E focus on fairness metrics that reveal disparities in predictive performance across patient subgroups, such as race, gender, or socioeconomic status, during program evaluations like risk stratification or intervention tracking.sparkco+1
Group Fairness Metrics
Disparate Impact Ratio (DIR) quantifies equal positive prediction rates across groups, flagging bias if <0.8 (e.g., lower high-risk flags for minorities despite equal needs). Equalized Odds (EO) measures similar true/false positive rates between groups; differences >0.1 indicate skewed diagnostic accuracy in M&E dashboards.ijsra+1
Calibration and Error Metrics
Calibration by Group assesses if predicted probabilities match actual outcomes per demographic—e.g., overconfident scores for majority groups signal bias, tracked via Brier scores pre/post-mitigation. Group Error Rate Parity (GERP) compares false positive/negative rates; >0.05 disparities (e.g., 25% higher false negatives for minorities) trigger alerts.censinet+2
Performance Breakdowns
| Metric | Definition | Detection Threshold | Healthcare M&E Example [sparkco] |
|---|---|---|---|
| DIR | Positive rates ratio (Group A/B) | <0.8 or >1.25 | Unequal care prioritization |
| EO Difference | TPR/FPR delta | >0.1 | |
| False Negative Rate (FNR) Gap | FNR A – FNR B | ||
| Demographic Parity | Equal selection rates | <0.8 | Resource allocation skew [censinet] |
These outcomes, validated in cases like Obermeyer’s algorithm, enable real-time M&E monitoring with 20-30% accuracy gains post-audit.arxiv+1
