MeasureFairness: Base Measure for Fairness

In mlr3fairness: Fairness Auditing and Debiasing for 'mlr3'

Base Measure for Fairness

Description

This measure extends mlr3::Measure() with statistical group fairness: A common approach to quantifying a model's fairness is to compute the difference between a protected and an unprotected group according w.r.t. some performance metric, e.g. ⁠classification error⁠ (mlr_measures_classif.ce) or ⁠false positive rate⁠ (mlr_measures_classif.fpr). The operation for comparison (e.g., difference or quotient) can be specified using the operation parameter, e.g. groupdiff_absdiff() or groupdiff_tau().

Composite measures encompasing multiple fairness metrics can be built using MeasureFairnessComposite.

Some popular predefined measures can be found in the dictionary mlr_measures.

Protected Attributes

The protected attribute is specified as a col_role in the corresponding Task():
⁠<Task>$col_roles$pta = "name_of_attribute"⁠
This also allows specifying more than one protected attribute, in which case fairness will be considered on the level of intersecting groups defined by all columns selected as a predicted attribute.

Super class

mlr3::Measure -> MeasureFairness

Public fields

base_measure

(Measure())
The base measure to be used by the fairness measures, e.g. mlr_measures_classif.fpr for the false positive rate.

operation

(⁠function()⁠)
The operation used to compute the difference. A function with args 'x' and 'y' that returns a single value. Defaults to abs(x - y).

Methods

Public methods

• MeasureFairness$new()

• MeasureFairness$clone()

Inherited methods

• mlr3::Measure$aggregate()
• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$print()
• mlr3::Measure$score()

---

Method new()

Creates a new instance of this R6 class.

Usage

MeasureFairness$new(
  id = NULL,
  base_measure,
  operation = groupdiff_absdiff,
  minimize = TRUE,
  range = c(-Inf, Inf)
)

Arguments

id

(character)
The measure's id. Set to 'fairness.<base_measure_id>' if ommited.

base_measure

(Measure())
The base metric evaluated within each subgroup.

operation

(function)
The operation used to compute the difference. A function that returns a single value given input: computed metric for each subgroup. Defaults to groupdiff_absdiff.

minimize

(logical())
Should the measure be minimized? Defaults to TRUE.

range

(numeric(2))
Range of the resulting measure. Defaults to c(-Inf, Inf).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureFairness$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

MeasureFairnessComposite

Examples

library("mlr3")
# Create MeasureFairness to measure the Predictive Parity.
t = tsk("adult_train")
learner = lrn("classif.rpart", cp = .01)
learner$train(t)
measure = msr("fairness", base_measure = msr("classif.ppv"))
predictions = learner$predict(t)
predictions$score(measure, task = t)

mlr3fairness documentation built on May 31, 2023, 7:22 p.m.