disparate_impact_remover: Disparate Impact Remover

In aif360: Help Detect and Mitigate Bias in Machine Learning Models

Description

Disparate impact remover is a preprocessing technique that edits feature values increase group fairness while preserving rank-ordering within groups

Usage

disparate_impact_remover(repair_level, sensitive_attribute)

Arguments

repair_level	Repair amount. 0.0 is no repair while 1.0 is full repair.
sensitive_attribute	Single protected attribute with which to do repair.

Examples

# An example using the Adult Dataset
load_aif360_lib()
ad <- adult_dataset()
p <- list("race", 1)
u <- list("race", 0)

di <- disparate_impact_remover(repair_level = 1.0, sensitive_attribute = "race")
rp <- di$fit_transform(ad)

di_2 <- disparate_impact_remover(repair_level = 0.8, sensitive_attribute = "race")
rp_2 <- di_2$fit_transform(ad)

aif360 documentation built on July 1, 2020, 5:34 p.m.