Equalised odds requires that the model’s true positive rate (TPR) and false positive rate (FPR) are consistent across protected subgroups. Differences in error rates mean the model makes systematically different types of mistakes for different groups, constituting unfair treatment even if overall accuracy is similar.

If a credit scoring model correctly identifies 90% of creditworthy applicants in one ethnic group but only 70% in another, it has unequal true positive rates. The group with the lower TPR is systematically disadvantaged: creditworthy individuals from that group are disproportionately denied. Similarly, if the model incorrectly classifies 5% of non-creditworthy applicants as creditworthy in one group but 15% in another, the FPR disparity means one group bears disproportionate cost from false approvals.

Computing equalised odds requires access to ground truth labels, which may not be available for all predictions in production. This makes it primarily a development-time and periodic-review metric. The AISDP records the TPR and FPR per protected subgroup, the parity thresholds applied, and any subgroups that fall outside the acceptable range.

The metric is computed as part of the fairness evaluation suite integrated into the CI pipeline. Fairlearn’s MetricFrame computes per-subgroup TPR and FPR and reports the disparity. Threshold breaches trigger the bias mitigation process and block deployment through the fairness gate.

Key outputs

• TPR and FPR per protected subgroup
• Parity assessment and threshold compliance
• Deployment gate status