Ethics✓ Mathematical
◆ The PatternTesting for bias across protected groups
Fairness audits measure whether your model treats different groups equitably. Demographic parity checks if positive rates are equal across groups. Equalized odds checks if error rates are equal. No single metric captures all fairness — you must choose which definition matches your context.
Disparate Impact = P(ŷ=1|G=a) / P(ŷ=1|G=b)
A ratio below 0.8 (the "four-fifths rule") suggests adverse impact against group b.
// Interactive — fairness metrics across groups
# Python — Fairlearn fairness assessment from fairlearn.metrics import MetricFrame from sklearn.metrics import accuracy_score mf = MetricFrame( metrics=accuracy_score, y_true=y_test, y_pred=y_pred, sensitive_features=demographics ) print(mf.by_group) print("Ratio:", mf.ratio())
Pattern bridge: Fairness audits apply confidence intervals per subgroup — the same statistical rigour, applied to equity. In markets, behavioral bias recognition teaches the same lesson: your defaults aren't neutral.