mlr_measures_fairness {mlr3fairness}

Fairness Measures in mlr3

Description

Fairness Measures in mlr3

Usage

mlr_measures_fairness

Format

An object of class data.table (inherits from data.frame) with 18 rows and 2 columns.

Value

A data.table containing an overview of available fairness metrics.

Predefined measures

mlr3fairness comes with a set of predefined fairness measures as listed below. For full flexibility, MeasureFairness can be used to construct classical group fairness measures based on a difference between a performance metrics across groups by combining a performance measure with an operation for measuring differences. Furthermore MeasureSubgroup can be used to measure performance in a given subgroup, or alternatively groupwise_metrics(measure, task) to instantiate a measure for each subgroup in a Task.

key	description
fairness.acc	Absolute differences in accuracy across groups
fairness.mse	Absolute differences in mean squared error across groups
fairness.fnr	Absolute differences in false negative rates across groups
fairness.fpr	Absolute differences in false positive rates across groups
fairness.tnr	Absolute differences in true negative rates across groups
fairness.tpr	Absolute differences in true positive rates across groups
fairness.npv	Absolute differences in negative predictive values across groups
fairness.ppv	Absolute differences in positive predictive values across groups
fairness.fomr	Absolute differences in false omission rates across groups
fairness.fp	Absolute differences in false positives across groups
fairness.tp	Absolute differences in true positives across groups
fairness.tn	Absolute differences in true negatives across groups
fairness.fn	Absolute differences in false negatives across groups
fairness.cv	Difference in positive class prediction, also known as Calders-Wevers gap or demographic parity
fairness.eod	Equalized Odds: Mean of absolute differences between true positive and false positive rates across groups
fairness.pp	Predictive Parity: Mean of absolute differences between ppv and npv across groups
fairness.acc_eod=.05	Accuracy under equalized odds < 0.05 constraint
fairness.acc_ppv=.05	Accuracy under ppv difference < 0.05 constraint

Examples

library("mlr3")
# Predefined measures:
mlr_measures_fairness$key