fairmetrics (version 1.0.3)

eval_stats_parity: Examine Statistical Parity of a Model

Description

This function assesses statistical parity - also known as demographic parity - in the predictions of a binary classifier across two groups defined by a sensitive attribute. Statistical parity compares the rate at which different groups receive a positive prediction, irrespective of the true outcome. It reports the Positive Prediction Rate (PPR) for each group, their differences, ratios, and bootstrap-based confidence regions.

Usage

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

Value

A list containing the following elements:

• PPR_Group1: Positive Prediction Rate for the first group

• PPR_Group2: Positive Prediction Rate for the second group

• PPR_Diff: Difference in Positive Prediction Rate

• PPR_Ratio: The ratio in Positive Prediction Rate between the two groups. If confidence intervals are computed (confint = TRUE):

• PPR_Diff_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in Positive Prediction Rate

• PPR_Ratio_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the ratio in Positive Prediction Rate

Arguments

data

Data frame containing the outcome, predicted outcome, and sensitive attribute

outcome

Name of the outcome variable, it must be binary

group

Name of the sensitive attribute

probs

Name of the predicted outcome variable

cutoff

Threshold for the predicted outcome, default is 0.5

confint

Whether to compute 95% confidence interval, default is TRUE

bootstraps

Number of bootstrap samples, default is 2500

alpha

The 1 - significance level for the confidence interval, default is 0.05

digits

Number of digits to round the results to, default is 2

message

Whether to print the results, default is TRUE

See Also

eval_cond_stats_parity

Examples

# \donttest{
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 sensitive attribute and day_28_flg as the outcome.
# We choose threshold = 0.41 so that the overall FPR is around 5%.

# Evaluate Statistical Parity
eval_stats_parity(
  data = test_data,
  outcome = "day_28_flg",
  group = "gender",
  probs = "pred",
  cutoff = 0.41
)
# }