fairmetrics (version 1.0.3)

eval_pred_equality: Examine Predictive Equality of a Model

Description

This function evaluates predictive equality, a fairness metric that compares the False Positive Rate (FPR) between groups defined by a sensitive attribute. It assesses whether individuals from different groups are equally likely to be incorrectly flagged as positive when they are, in fact, negative.

Usage

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

Value

A list containing the following elements:

• FPR_Group1: False Positive Rate for the first group

• FPR_Group2: False Positive Rate for the second group

• FPR_Diff: Difference in False Positive Rate

• FPR_Ratio: Ratio in False Positive Rate If confidence intervals are computed (confint = TRUE):

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

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

alpha

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

bootstraps

Number of bootstrap samples, default is 2500

digits

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

message

Whether to print the results, default is TRUE

See Also

eval_pos_pred_parity, eval_neg_pred_parity, eval_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 Predictive Equality
eval_pred_equality(
  data = test_data,
  outcome = "day_28_flg",
  group = "gender",
  probs = "pred",
  cutoff = 0.41
)
# }