yardstick (version 1.3.1)

demographic_parity: Demographic parity

Description

Demographic parity is satisfied when a model's predictions have the same predicted positive rate across groups. A value of 0 indicates parity across groups. Note that this definition does not depend on the true outcome; the truth argument is included in outputted metrics for consistency.

demographic_parity() is calculated as the difference between the largest and smallest value of detection_prevalence() across groups.

Demographic parity is sometimes referred to as group fairness, disparate impact, or statistical parity.

See the "Measuring Disparity" section for details on implementation.

Usage

demographic_parity(by)

Value

This function outputs a yardstick fairness metric function. Given a grouping variable by, demographic_parity() will return a yardstick metric function that is associated with the data-variable grouping by and a post-processor. The outputted function will first generate a set of detection_prevalence metric values by group before summarizing across groups using the post-processing function.

The outputted function only has a data frame method and is intended to be used as part of a metric set.

Arguments

by

The column identifier for the sensitive feature. This should be an unquoted column name referring to a column in the un-preprocessed data.

Measuring Disparity

By default, this function takes the difference in range of detection_prevalence .estimates across groups. That is, the maximum pair-wise disparity between groups is the return value of demographic_parity()'s .estimate.

For finer control of group treatment, construct a context-aware fairness metric with the new_groupwise_metric() function by passing a custom aggregate function:

# the actual default `aggregate` is:
diff_range <- function(x, ...) {diff(range(x$.estimate))}
demographic_parity_2 <-
  new_groupwise_metric(
    fn = detection_prevalence,
    name = "demographic_parity_2",
    aggregate = diff_range
  )

In aggregate(), x is the metric_set() output with detection_prevalence values for each group, and ... gives additional arguments (such as a grouping level to refer to as the "baseline") to pass to the function outputted by demographic_parity_2() for context.

References

Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J., & Wallach, H. (2018). "A Reductions Approach to Fair Classification." Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research. 80:60-69.

Verma, S., & Rubin, J. (2018). "Fairness definitions explained". In Proceedings of the international workshop on software fairness (pp. 1-7).

Bird, S., Dudík, M., Edgar, R., Horn, B., Lutz, R., Milan, V., ... & Walker, K. (2020). "Fairlearn: A toolkit for assessing and improving fairness in AI". Microsoft, Tech. Rep. MSR-TR-2020-32.

See Also

Other fairness metrics: equal_opportunity(), equalized_odds()

Examples

library(dplyr)

data(hpc_cv)

head(hpc_cv)

# evaluate `demographic_parity()` by Resample
m_set <- metric_set(demographic_parity(Resample))

# use output like any other metric set
hpc_cv %>%
  m_set(truth = obs, estimate = pred)

# can mix fairness metrics and regular metrics
m_set_2 <- metric_set(sens, demographic_parity(Resample))

hpc_cv %>%
  m_set_2(truth = obs, estimate = pred)