eval_acc_parity: Examine Accuracy Parity of a Model

In fairmetrics: Fairness Evaluation Metrics with Confidence Intervals for Binary Protected Attributes

Examine Accuracy Parity of a Model

Description

This function assesses Accuracy Parity, a fairness criterion that evaluates whether the overall accuracy of a predictive model is consistent across two groups defined by a binary protected attribute.

Usage

eval_acc_parity(
  data,
  outcome,
  group,
  probs,
  cutoff = 0.5,
  confint = TRUE,
  alpha = 0.05,
  bootstraps = 2500,
  digits = 2,
  message = TRUE
)

Arguments

data	Data frame containing the outcome, predicted outcome, and binary protected attribute
outcome	Name of the outcome variable
group	Name of the binary protected attribute. Must consist of only two groups.
probs	Predicted probabilities
cutoff	Cutoff value for the predicted probabilities
confint	Logical indicating whether to calculate confidence intervals
alpha	The 1 - significance level for the confidence interval, default is 0.05
bootstraps	Number of bootstraps to use for confidence intervals
digits	Number of digits to round the results to, default is 2
message	Logical; if TRUE (default), prints a textual summary of the fairness evaluation. Only works if confint is TRUE.

Value

A list containing the following elements:

• Accuracy for Group 1

• Accuracy for Group 2

• Difference in accuracy

• Ratio in accuracy If confidence intervals are computed (confint = TRUE):

• A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in accuracy

• A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the ratio in accurac

See Also

eval_cond_acc_equality

Examples

library(fairmetrics)
library(dplyr)
library(magrittr)
library(randomForest)
data("mimic_preprocessed")
set.seed(123)
train_data <- mimic_preprocessed %>%
  dplyr::filter(dplyr::row_number() <= 700)
# Fit a random forest model
rf_model <- randomForest::randomForest(factor(day_28_flg) ~ ., data = train_data, ntree = 1000)
# Test the model on the remaining data
test_data <- mimic_preprocessed %>%
  dplyr::mutate(gender = ifelse(gender_num == 1, "Male", "Female")) %>%
  dplyr::filter(dplyr::row_number() > 700)

test_data$pred <- predict(rf_model, newdata = test_data, type = "prob")[, 2]

# Fairness evaluation
# We will use sex as the protected attribute and day_28_flg as the outcome.
# We choose threshold = 0.41 so that the overall FPR is around 5%.

# Evaluate Accuracy Parity
eval_acc_parity(
  data = test_data,
  outcome = "day_28_flg",
  group = "gender",
  probs = "pred",
  cutoff = 0.41
)

fairmetrics documentation built on April 16, 2026, 5:07 p.m.