eval_bs_parity: Examine Brier Score Parity of a Model

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

Examine Brier Score Parity of a Model

Description

This function evaluates Brier Score Parity, a fairness measure that checks whether the Brier score (a measure of the calibration of probabilistic predictions) is similar across across two groups defined by a binary protected attribute. Brier score parity ensures that the model's predicted probabilities are equally well calibrated across subpopulations.

Usage

eval_bs_parity(
  data,
  outcome,
  group,
  probs,
  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
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:

• Brier Score for Group 1

• Brier Score for Group 2

• Difference in Brier Score

• Ratio in Brier Score 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 Brier Score

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

See Also

eval_acc_parity, eval_cond_acc_equality, eval_pos_pred_parity, eval_neg_pred_parity

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.

# Evaluate Brier Score Parity
eval_bs_parity(
  data = test_data,
  outcome = "day_28_flg",
  group = "gender",
  probs = "pred"
)

fairmetrics documentation built on Sept. 11, 2025, 9:09 a.m.