Compute Fairness Metrics for Binary Classification — get_fairness

Computes a comprehensive set of fairness metrics for binary classification models, disaggregated by a sensitive attribute (e.g., race, gender). Optionally, conditional fairness can be evaluated using a second attribute and a specified condition. The function also computes corresponding performance metrics used in the fairness calculations.

Usage

get_fairness_metrics(
  data,
  outcome,
  group,
  group2 = NULL,
  condition = NULL,
  probs,
  confint = TRUE,
  cutoff = 0.5,
  bootstraps = 2500,
  alpha = 0.05,
  digits = 2
)

Arguments

data: A data frame containing the outcome, group, and predicted probabilities.
outcome: The name of the column containing the true binary outcome.
group: The name of the column representing the sensitive attribute (e.g., race, gender).
group2: Define if conditional statistical parity is desired. Name of a secondary group variable used for conditional fairness analysis.
condition: Define if conditional statistical parity is desired. If the conditional group is categorical, the condition supplied must be a character of the levels to condition on. If the conditional group is continuous, the conditions supplied must be a character containing the sign of the condition and the value to threshold the continuous variable (e.g. "<50", ">50", "<=50", ">=50").
probs: The name of the column with predicted probabilities.
confint: Logical indicating whether to calculate confidence intervals.
cutoff: Numeric threshold for classification. Default is 0.5.
bootstraps: Number of bootstrap samples. Default is 2500.
alpha: Significance level for confidence intervals. Default is 0.05.
digits: Number of digits to round the metrics to. Default is 2.

Value

A list containing:

performance: Data frame with performance metrics by group.
fairness: Data frame with computed fairness metrics and optional confidence intervals.

Details

The results are returned as a list of two data frames:

performance: Contains performance metrics (e.g., TPR, FPR, PPV) by group.
fairness: Contains group-level fairness metrics (e.g., disparities or ratios), confidence intervals (if specified).

Fairness Metrics Included:

Statistical Parity: Difference in positive prediction rates across groups.
Conditional Statistical Parity (if group2 and condition are specified): Parity conditioned on a second group and value.
Equal Opportunity: Difference in true positive rates (TPR) across groups.
Predictive Equality: Difference in false positive rates (FPR) across groups.
Balance for Positive Class: Checks whether the predicted probability distributions for positive outcomes are similar across groups.
Balance for Negative Class: Same as above, but for negative outcomes.
Positive Predictive Parity: Difference in positive predictive values (precision) across groups.
Negative Predictive Parity: Difference in negative predictive values across groups.
Brier Score Parity: Difference in Brier scores across groups.
Overall Accuracy Parity: Difference in overall accuracy across groups.
Treatment Equality: Ratio of false negatives to false positives across groups.

Examples

# \donttest{
library(fairmetrics)
library(dplyr)
library(randomForest)
library(magrittr)
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%.

# Get Fairness Metrics
get_fairness_metrics(
 data = test_data,
 outcome = "day_28_flg",
 group = "gender",
 group2 = "age",
 condition = ">=60",
 probs = "pred",
 confint = TRUE,
 cutoff = 0.41,
 alpha = 0.05
)
#> $performance
#>                                     Metric GroupFemale GroupMale
#> 1                 Positive Prediction Rate        0.17      0.08
#> 2                 Positive Prediction Rate        0.34      0.21
#> 3                      False Negative Rate        0.38      0.62
#> 4                      False Positive Rate        0.08      0.03
#> 5                     Avg. Predicted Prob.        0.46      0.37
#> 6                     Avg. Predicted Prob.        0.15      0.10
#> 7                Positive Predictive Value        0.62      0.66
#> 8                Negative Predictive Value        0.92      0.90
#> 9                              Brier Score        0.09      0.08
#> 10                                Accuracy        0.87      0.88
#> 11 (False Negative)/(False Positive) Ratio        1.03      3.24
#> 
#> $fairness
#>                            Metirc Difference    95% Diff CI Ratio 95% Ratio CI
#> 1              Statistical Parity       0.09   [0.05, 0.13]  2.12 [1.48, 3.05]
#> 2  Conditional Statistical Parity       0.13   [0.05, 0.21]  1.62 [1.18, 2.22]
#> 3               Equal Opportunity      -0.24 [-0.39, -0.09]  0.61 [0.44, 0.86]
#> 4             Predictive Equality       0.05   [0.02, 0.08]  2.67  [1.39, 5.1]
#> 5      Balance for Positive Class       0.09   [0.04, 0.14]  1.24 [1.09, 1.41]
#> 6      Balance for Negative Class       0.05   [0.03, 0.07]  1.50 [1.29, 1.74]
#> 7      Positive Predictive Parity      -0.04  [-0.21, 0.13]  0.94 [0.72, 1.23]
#> 8      Negative Predictive Parity       0.02  [-0.15, 0.19]  1.02 [0.79, 1.33]
#> 9              Brier Score Parity       0.01  [-0.01, 0.03]  1.12 [0.88, 1.43]
#> 10        Overall Accuracy Parity      -0.01  [-0.05, 0.03]  0.99 [0.94, 1.04]
#> 11             Treatment Equality      -2.21   [-4.5, 0.08]  0.32 [0.15, 0.69]
#> 
# }