For binary classification systems, the selection rate ratio is the simplest and most widely understood fairness metric. It computes the positive outcome rate for each protected subgroup, divides each by the positive outcome rate for the majority group, and flags any ratio below 0.80 (the four-fifths rule). This metric has regulatory heritage from US employment law, where it has served as a screening device for adverse impact for decades. The four-fifths rule does not have specific regulatory status under EU law; the AI Act does not prescribe fairness thresholds. The 0.80 threshold is used here as an industry convention with broad practitioner acceptance, and organisations should calibrate it to their system’s risk profile and deployment context.
The AISDP reports selection rate ratios for all measured subgroups, with a clear indication of which subgroups meet the 0.80 threshold and which do not. The report should include confidence intervals to indicate the statistical reliability of the ratios, particularly for smaller subgroups where sampling variation may produce misleading values.
The four-fifths rule’s limitation is that it measures only outcome rates, not outcome quality. A model that gives positive outcomes to the same proportion of each subgroup but makes systematically worse predictions for one subgroup (more false positives, more false negatives) would pass the four-fifths test while still being unfair. The selection rate ratio is therefore a necessary but insufficient fairness check; it is complemented by the equalised odds, predictive parity, calibration, and individual fairness metrics.
Where the selection rate ratio falls below 0.80 for any subgroup, the finding triggers the bias mitigation process. The threshold is configurable; some organisations may adopt a stricter threshold (0.90) based on the AI Governance Lead’s assessment of the system’s risk profile.
Key outputs
- Selection rate ratio report per protected subgroup
- Threshold compliance indication per subgroup
- Confidence intervals for smaller subgroups