## Fairness Measures

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 given by a protected attribute (Barocas, Hardt, and Narayanan 2019; Hardt, Price, and Srebro 2016).

• Individual Fairness: Basically requires that similar people are treated similar independent of the protected attribute (Dwork et al. 2012). We will briefly introduce individual fairness in a dedicated section below.

• 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, mlr3fairness currently does not provide any causal fairness metrics (Kilbertus et al. 2017).

### Statistical Group Fairness Metrics

One 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: Mean of absolute differences between true positive and false positive rates across groups
fairness.pp Predictive Parity: Mean 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 resource 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.

library(mlr3fairness)
library(mlr3learners)

t = tsk("adult_train")$filter(1:1000) t$col_roles$pta #>  "sex" Our model is a random forest model fitted on the dataset: l = lrn("classif.ranger") l$train(t)

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

test = tsk("adult_test")
prd = l$predict(test) Using the $score method and a measure we can e.g. compute the absolute differences in true positive rates.

prd$score(msr("fairness.tpr"), task = test) #> fairness.tpr #> 0.08450268 The exact measure to choose is often data-set and situation dependent. The Aequitas Fairness Tree can be a great ressource to get started. We can furthermore simply look at the per-group measures: meas = groupwise_metrics(msr("classif.tpr"), test) prd$score(meas, task = test)
#>   subgroup.tpr_Male subgroup.tpr_Female
#>           0.8924308           0.9769335

### 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.

# Binary Class false positive rates
msr("classif.fpr")
#> <MeasureBinarySimple:classif.fpr>: False Positive Rate
#> * Packages: mlr3, mlr3measures
#> * Range: [0, 1]
#> * Minimize: TRUE
#> * Average: macro
#> * Parameters: list()
#> * Properties: -
#> * Predict type: response

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:

### How to compare the fairness performance between different learners using Benchmarks

In this example, we create a BenchmarkInstance. Then by using aggregate() function they could access the fairness measures easily. The following example demonstrates the process to evaluate the fairness metrics on Benchmark Results:

design = benchmark_grid(
learners = lrns(c("classif.ranger", "classif.rpart"),
predict_type = "prob", predict_sets = c("train", "test")),
resamplings = rsmps("cv", folds = 3)
)

bmr = benchmark(design)

# Operations have been set to groupwise_quotient()
measures = list( msr("fairness.tpr"), msr("fairness.npv"), msr("fairness.acc"), msr("classif.acc") )

tab = bmr$aggregate(measures) tab #> nr task_id learner_id resampling_id iters fairness.tpr fairness.npv #> 1: 1 adult_train classif.ranger cv 3 0.06503631 0.01982795 #> 2: 2 adult_train classif.rpart cv 3 0.05976698 0.05344223 #> fairness.acc classif.acc #> 1: 0.1052386 0.8607005 #> 2: 0.1216803 0.8405170 #> Hidden columns: resample_result ### Metrics aggregation - groupdiff_* For MeasureFairness, mlr3 computes the base_measure in each group specified by the pta column. If we now want to return those measures, we need to aggregate this to a single metric - e.g. using one of the groupdiff_* functions available with mlr3. See ?groupdiff_tau for a list. Note, that the operation below also accepts custom aggregation function, see the example below. msr("fairness.acc", operation = groupdiff_diff) #> <MeasureFairness:fairness.acc> #> * Packages: mlr3, mlr3fairness #> * Range: [0, 1] #> * Minimize: TRUE #> * Average: macro #> * Parameters: list() #> * Properties: requires_task #> * Predict type: response We can also report other metrics, e.g. the error in a specific group: t = tsk("adult_train")$filter(1:1000)
mm = msr("fairness.acc", operation = function(x) {x["Female"]})
l = lrn("classif.rpart")
prds = l$train(t)$predict(t)
prds$score(mm, t) #> fairness.acc #> 0.9404389 ### Individual Fairness Individual fairness notions were first proposed by (Dwork et al. 2012). The core idea comes from the principle of treating similar cases similarly and different cases differently. In contrast to statistical group fairness notions, this notion allows assessing fairness at an individual level and would therefore allow determining whether an individual is treated fairly. A more in-depth treatment of individual fairness notions is given by (Binns 2020). In order to translate this from an abstract concept into practice, we need to define two distance metrics: - A distance metric $$d(x_i, x_j)$$ that measures how similar two cases $$x_i$$ and $$x_j$$ are - A distance metric between treatments, here the predictions of our model $$f$$: $$\phi(f({x}_i), f({x}_j))$$. Intuitively, we would now want, that if $$d(x_i, x_j)$$ is small, the difference in predictions $$\phi(f({x}_i), f({x}_j))$$ should also be small. This essentially requires Lipschitz continuity of $$f$$ with respect to $$d$$. Given a Lipschitz constant $$L > 0$$, we can write this as: $\phi(f({x}_i), f({x}_j)) \leq L \cdot d(x_i, x_j).$ Currently, mlr3fairness does not support individual fairness metrics, but we aim to introduce such metrics in the future. ### Using metrics for non-mlr3 predictions We can similarly employ mlr3 metrics on predictions stemming from different models. To do so, we create a data.table containing the different components. # Get adult data as a data.table train = tsk("adult_train")$data()
mod = rpart::rpart(target ~ ., train)

# Predict on test data
test = tsk("adult_test")$data() yhat = predict(mod, test, type = "vector") # Convert to a factor with the same levels yhat = as.factor(yhat) levels(yhat) = levels(test$target)

compute_metrics(
data = test,
target = "target",
prediction = yhat,
protected_attribute = "sex",
metrics = msr("fairness.acc")
)
#> fairness.acc
#>    0.1248581
Barocas, Solon, Moritz Hardt, and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org.
Binns, Reuben. 2020. “On the Apparent Conflict Between Individual and Group Fairness.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 514–24. FAT* ’20.
Dwork, Cynthia, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. “Fairness Through Awareness.” In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, 214–26.
Hardt, Moritz, Eric Price, and Nati Srebro. 2016. “Equality of Opportunity in Supervised Learning.” Advances in Neural Information Processing Systems 29: 3315–23.
Kilbertus, Niki, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. 2017. “Avoiding Discrimination Through Causal Reasoning.” Advances in Neural Information Processing Systems 30.