fairlearn.metrics.demographic_parity_difference#

fairlearn.metrics.demographic_parity_difference(y_true, y_pred, *, sensitive_features, method='between_groups', sample_weight=None)[source]#

Calculate the demographic parity difference.

The demographic parity difference is defined as the difference between the largest and the smallest group-level selection rate, \(E[h(X) | A=a]\), across all values \(a\) of the sensitive feature(s). The demographic parity difference of 0 means that all groups have the same selection rate.

Read more in the User Guide.

Parameters:

  • y_true (array-like) – Ground truth (correct) labels.

  • y_pred (array-like) – Predicted labels \(h(X)\) returned by the classifier.

  • sensitive_features – The sensitive features over which demographic parity should be assessed

  • method (str) – How to compute the differences. See fairlearn.metrics.MetricFrame.difference() for details.

  • sample_weight (array-like) – The sample weights

Returns:

The demographic parity difference

Return type:

float