fairlearn.reductions.FalsePositiveRateParity#

class fairlearn.reductions.FalsePositiveRateParity(*, difference_bound=None, ratio_bound=None, ratio_bound_slack=0.0)[source]#

Implementation of false positive rate parity as a moment.

Adds conditioning on label Y=0 compared to demographic parity, i.e.,

\[P[h(X) = 1 | A = a, Y = 0] = P[h(X) = 1 | Y = 0] \; \forall a\]

This implementation of UtilityParity defines the event corresponding to Y=0.

The prob_event pandas.DataFrame will record the fraction of the samples corresponding to Y = 0 in the Y array.

The index MultiIndex will have a number of entries equal to the number of unique values of the sensitive feature, multiplied by the number of unique non-NaN values of the constructed event array, whose entries are either NaN or label=0 (so only one unique non-NaN value), multiplied by two (for the Lagrange multipliers for positive and negative constraints).

With these definitions, the UtilityParity.signed_weights() method will calculate the costs for Y=0 as they are calculated in Example 4 of Agarwal et al.[#agarwal2018reductions]_, but will use the weights equal to zero for Y=1.

This Moment also supports control features, which can be used to stratify the data, with the constraint applied within each stratum, but not between strata.