roc_parity: ROC AUC parity

In fairness: Algorithmic Fairness Metrics

Description

This function computes the ROC AUC parity metric

Usage

roc_parity(data, outcome, group, probs, base = NULL, group_breaks = NULL)

Arguments

data	Data.frame that contains the necessary columns.
outcome	Column name indicating the binary outcome variable (character).
group	Column name indicating the sensitive group (character).
probs	Column name or vector with the predicted probabilities (numeric between 0 - 1).
base	Base level of the sensitive group (character).
group_breaks	If group is continuous (e.g., age): either a numeric vector of two or more unique cut points or a single number >= 2 giving the number of intervals into which group feature is to be cut.

Details

This function computes the ROC AUC values for each subgroup. In the returned table, the reference group will be assigned 1, while all other groups will be assigned values according to whether their ROC AUC values are lower or higher compared to the reference group. Lower ROC AUC will be reflected in numbers lower than 1 in the returned named vector, thus numbers lower than 1 mean WORSE prediction for the subgroup.

Value

Metric	Raw ROC AUC metrics for all groups and metrics standardized for the base group (parity metric). Lower values compared to the reference group mean lower ROC AUC values in the selected subgroups
Metric_plot	Bar plot of ROC AUC metric
Probability_plot	Density plot of predicted probabilities per subgroup
ROCAUC_plot	ROC plots for all subgroups

Examples

data(compas)
compas$Two_yr_Recidivism_01 <- ifelse(compas$Two_yr_Recidivism == 'yes', 1, 0) 
roc_parity(data = compas, outcome = 'Two_yr_Recidivism_01', group = 'ethnicity',
probs = 'probability', base = 'Caucasian')
roc_parity(data = compas, outcome = 'Two_yr_Recidivism_01', group = 'ethnicity',
probs = 'probability', base = 'African_American')

fairness documentation built on April 14, 2021, 5:09 p.m.