holisticai.bias.metrics.no_disparate_impact_level#

holisticai.bias.metrics.no_disparate_impact_level(group_a, group_b, y_pred)[source]#

No disparate impact level

This function computes the maximum score such that thresholding at that score does not allow adverse impact.

Interpretation

Parameters

group_aarray-like: Group membership vector (binary)
group_barray-like: Group membership vector (binary)
y_predarray-like: Predictions vector (regression)

Returns

float: No disparate impact level

Examples

>>> import numpy as np
>>> from holisticai.bias.metrics import no_disparate_impact_level
>>> group_a = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0])
>>> group_b = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
>>> y_pred = np.array([0.8, 0.9, 0.2, 0.1, 0.7, 0.9, 0.8, 0.6, 0.3, 0.5])
>>> no_disparate_impact_level(group_a, group_b, y_pred)
0.7