acc_parity: Accuracy parity

In kozodoi/Fairness: Algorithmic Fairness Metrics

Accuracy parity

Description

This function computes the Accuracy parity metric

Formula: (TP + TN) / (TP + FP + TN + FN)

Usage

acc_parity(
  data,
  outcome,
  group,
  probs = NULL,
  preds = NULL,
  outcome_base = NULL,
  cutoff = 0.5,
  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). Either probs or preds need to be supplied.
preds	Column name or vector with the predicted binary outcome (0 or 1). Either probs or preds need to be supplied.
outcome_base	Base level of the outcome variable (i.e., negative class). Default is the first level of the outcome variable.
cutoff	Cutoff to generate predicted outcomes from predicted probabilities. Default set to 0.5.
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 Accuracy parity metric as described by Friedler et al., 2018. Accuracy metrics are calculated by the division of correctly predicted observations (the sum of all true positives and true negatives) with the number of all predictions. In the returned named vector, the reference group will be assigned 1, while all other groups will be assigned values according to whether their accuracies are lower or higher compared to the reference group. Lower accuracies 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 accuracy metrics for all groups and metrics standardized for the base group (accuracy parity metric). Lower values compared to the reference group mean lower accuracies in the selected subgroups
Metric_plot	Bar plot of Accuracy parity metric
Probability_plot	Density plot of predicted probabilities per subgroup. Only plotted if probabilities are defined

Examples

data(compas)
compas$Two_yr_Recidivism_01 <- ifelse(compas$Two_yr_Recidivism == 'yes', 1, 0) 
acc_parity(data = compas, outcome = 'Two_yr_Recidivism_01', group = 'ethnicity',
probs = 'probability', cutoff = 0.4, base = 'Caucasian')
acc_parity(data = compas, outcome = 'Two_yr_Recidivism_01', group = 'ethnicity',
preds = 'predicted', cutoff = 0.5, base = 'Hispanic')

kozodoi/Fairness documentation built on March 22, 2023, 12:29 p.m.