yardstick (version 1.3.1)

equal_opportunity: Equal opportunity

Description

Equal opportunity is satisfied when a model's predictions have the same true positive and false negative rates across protected groups. A value of 0 indicates parity across groups.

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

Equal opportunity is sometimes referred to as equality of opportunity.

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

Usage

equal_opportunity(by)

Value

This function outputs a yardstick fairness metric function. Given a grouping variable by, equal_opportunity() 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 sens 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 sens .estimates across groups. That is, the maximum pair-wise disparity between groups is the return value of equal_opportunity()'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))}
equal_opportunity_2 <-
  new_groupwise_metric(
    fn = sens,
    name = "equal_opportunity_2",
    aggregate = diff_range
  )

In aggregate(), x is the metric_set() output with sens 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 equal_opportunity_2() for context.

References

Hardt, M., Price, E., & Srebro, N. (2016). "Equality of opportunity in supervised learning". Advances in neural information processing systems, 29.

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: demographic_parity(), equalized_odds()

Examples

library(dplyr)

data(hpc_cv)

head(hpc_cv)

# evaluate `equal_opportunity()` by Resample
m_set <- metric_set(equal_opportunity(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, equal_opportunity(Resample))

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