# FairnessMeasures

Fairness measures (or metrics) allow us to assess and audit for possible biases in a trained model. There are several types of metrics that are widely used in order to assess a model’s fairness. They can be coarsely classified into three groups:

• Statistical Group Fairness Metrics: Given a set of predictions from our model, we assess for differences in one or multiple metrics across groups.

• Individual Fairness: Basically requires that similar people are treated similar independent of the protected attribute. This is more of a philosophical concept and concrete implementations of this fairness notion are not immediately clear.

• Causal Fairness Notions: An important realization in the context of Fairness is, that whether a process is fair is often subject to the underlying causal directed acyclic graph (DAG). Knowledge of the DAG allows for causally assessing reasons for (un-)fairness. Since DAG’s are often hard to construct for a given dataset, we currently do not provide any causal fairness metrics.

## Core Idea: Statistical Group Fairness

A simple way to assess the fairness of a model is to find a definition of fairness that is relevant to a problem at hand. We might for example define a model to be fair if the chance to be accepted for a job given you are qualified is independent of a protected attribute e.g. gender. This can e.g. be measured using the true positive rate(TPR): in mlr3 this metric is called "classif.tpr". In this case we measure discrepancies between groups by computing differences (-) but we could also compute quotients. In practice, we often compute absolute differences.

$\Delta_{TPR} = TPR_{Group 1} - TPR_{Group 2}$

We will use metrics like the one defined above throughout the remainder of this vignette. Some predefined measures are readily available in mlr3fairness, but we will also showcase how custom measures can be constructed below.

In general, fairness measures have a fairness. prefix followed by the metric that is compared across groups. We will thus e.g. call the difference in accuracies across groups fairness.acc. A full list can be found below.

key description
fairness.acc Absolute differences in accuracy across groups
fairness.mse Absolute differences in mean squared error across groups
fairness.fnr Absolute differences in false negative rates across groups
fairness.fpr Absolute differences in false positive rates across groups
fairness.tnr Absolute differences in true negative rates across groups
fairness.tpr Absolute differences in true positive rates across groups
fairness.npv Absolute differences in negative predictive values across groups
fairness.ppv Absolute differences in positive predictive values across groups
fairness.fomr Absolute differences in false omission rates across groups
fairness.fp Absolute differences in false positives across groups
fairness.tp Absolute differences in true positives across groups
fairness.tn Absolute differences in true negatives across groups
fairness.fn Absolute differences in false negatives across groups
fairness.cv Difference in positive class prediction, also known as Calders-Wevers gap or demographic parity
fairness.eod Equalized Odds: Sum of absolute differences between true positive and false positive rates across groups
fairness.pp Predictive Parity: Sum of absolute differences between ppv and npv across groups
fairness.acc_eod=.05 Accuracy under equalized odds < 0.05 constraint
fairness.acc_ppv=.05 Accuracy under ppv difference < 0.05 constraint

## Assessing for Bias: A first look

This vignette assumes that you are familiar with the basics of mlr3 and it’s core objects. The mlr3 book can be a great ressource in case you want to learn more about mlr3’s internals.

We first start by training a model for which we want to conduct an audit. For this example, we use the adult_train dataset. Keep in mind all the datasets from mlr3fairness package already set protected attribute via the col_role “pta”, here the “sex” column. To speed things up, we only use the first 1000 rows.

Our model is a random forest model fitted on the dataset:

We can now predict on a new dataset and use those predictions to assess for bias:

Using the \$score method and a measure we can e.g. compute the absolute differences in true positive rates.

The exact measure to choose is often data-set and situation dependent. The Aequitas Fairness Tree can be a great ressource to get started.

We can furthermore simply look at the per-group measures:

## Fairness Measures - An in-depth look

Before, we have used msr("fairness.tpr") to assess differences in false positive rates across groups. But what happens internally?

The msr() function is used to obtain a Measure from a dictionary of pre-defined Measures. We can use msr() without any arguments in order to print a list of available measures. In the following example, we will build a Measure that computes differences in False Positive Rates making use of the "classif.fpr" measure readily implemented in mlr3.

The core Measure in mlr3fairness is a MeasureFairness. It can be used to construct arbitrary measures that compute a difference between a specific metric across groups. We can therefore build a new metric as follows:

This measure does the following steps: - Compute the metric supplied as base_measure in each group defined by the "pta" column. - Compute operation (here abs(x[1] - x[2])) and return the result.

In some cases, we might also want to replace the operation with a different operation, e.g. x[1] / x[2] in order to compute a different perspective.

mlr3fairness comes with two built-in functions that can be used to compute fairness metrics also across protected attributes that have more than two classes.

• groupdiff_absdiff: maximum absolute difference between all classes (the default for all metrics)
• groupdiff_tau: minimum quotient between all classes

Note: Depending on the operation we need to set a different minimize flag for the measure, so subsequent operations based on the measure automatically know if the measure is to be minimized or maximized e.g. during tuning.

We can also use those operations to construct a measure using msr(), since MeasureFairness (key: msr("fairness")) can be constructed from the dictionary with additional arguments.

This allows us to construct pretty flexible metrics e.g. for regression settings:

### Non-binary protected groups

While fairness measures are widely defined or used with binary protected attributes, we can easily extend fairness measures such that they work with non-binary valued protected attributes.

In order to do this, we have to supply an operation that reduces the desired metric measured in each subgroup to a single value. Two examples for such operations are groupdiff_absdiff and groupdiff_tau but custom functions can also be applied. Note, that mlr3 Measures are designed for a scalar output and operation therefore always has to result in a single scalar value.

## Composite Fairness Measures

Some fairness measures also require a combination of multiple Fairness Metrics. In the following example we show how to compute the mean of two fairness metrics, here false negative and true negative rates and create a new Measure that computes the mean (see aggfun) of those metrics:

## Metrics aggregation - groupdiff_*

For MeasureFairness, mlr3 computes the base_measure in each group specified by the pta column. If we now want to return those measures, we need to aggregate this to a single metric - e.g. using one of the groupdiff_* functions available with mlr3. See ?groupdiff_tau for a list. Note, that the operation below also accepts custom aggregation function, see the example below.

We can also report other metrics, e.g. the error in a specific group: