Introduction

Humans are increasingly subject to data-driven automated decision-making. Those automated procedures, such as credit risk assessments, are often applied using predictive models (Kozodoi, Jacob, and Lessmann 2022; Galindo and Tamayo 2000), often profoundly affecting individual’s lives. It is therefore important that, along with tools to develop and improve such models, we also develop sufficient capabilities to analyze and assess models not only with respect to their robustness and predictive performance but also with respect to potential biases. This is highlighted by the European General Data Protection Regulation (GDPR) which requires data to be processed fairly. Popular modelling frameworks for the R language (R Core Team 2021) such as (Kuhn 2021), (Kuhn and Wickham 2020), (Polley et al. 2021), or (Bischl et al. 2016) implement a plethora of metrics to measure performance, but fairness metrics are widely missing. This lack of availability can be detrimental to obtaining fair and unbiased models if the result is to forgo bias audits due to the considerable complexity of implementing such metrics. Consequently, there exists a considerable necessity for R packages to (a) implement such metrics, and (b) to connect these metrics to existing ML frameworks. If biases are detected and need to be mitigated, we might furthermore want to employ bias mitigation techniques that tightly integrate with the fitting and evaluation of the resulting models in order to obtain trade-offs between a model’s fairness and utility (e.g., predictive accuracy).

In this article, we present the package which builds upon the ML framework (Lang et al. 2019). Our extension contains fairness metrics, fairness visualizations, and model-agnostic pre- and post-processing operators that aim to reduce biases in ML models. Additionally, comes with reporting functionality that assists the user in documenting data and ML models, as well as in performing fairness audits.

In the remainder of the article, we first provide an introduction to fairness in ML to raise awareness of biases that can arise due to the use of ML models. Next, we introduce the package, followed by an extensive case study, showcasing the capabilities of . We conclude with a summary.

Fairness in Machine Learning

Studies have found that data-driven automated decision-making systems often improve over human expertise (Dawes, Faust, and Meehl (1989)) and high-stakes decisions can therefore be enhanced using data-driven systems. This often does not only improve predictions, but can also make decisions more efficient through automation. Such systems, often without human oversight, are now ubiquitous in everyday life (O’neil 2016; Eubanks 2018; Noble 2018). To provide further examples, ML-driven systems are used for highly influential decisions such as loan accommodations (Chen 2018; Turner and McBurnett 2019), job applications (Schumann et al. 2020), healthcare (Topol 2019), and criminal sentencing (Angwin et al. 2016; Corbett-Davies et al. 2017; Berk et al. 2018). With this proliferation, such decisions have become subject to scrutiny as a result of prominent inadequacies or failures, for example in the case of the COMPAS recidivism prediction system (Angwin et al. 2016).

Without proper auditing, those models can unintentionally result in negative consequences for individuals, often from underprivileged groups (Barocas, Hardt, and Narayanan 2019). Several sources of such biases are worth mentioning in this context: Data often contains historical biases such as gender or racial stereotypes, that – if picked up by the model – will be replicated into the future. Similarly, unprivileged populations are often not represented in data due to sampling biases leading to models that perform well in groups sufficiently represented in the data but worse on others (Buolamwini and Gebru 2018) – this includes a higher rate of missing data. Other biases include biases in how labels and data are measured (Bao et al. 2021) as well as feedback loops where repeated decisions affect the population subject to such decisions. For an in-depth discussion and further sources of biases, the interested reader is referred to available surveys of the field (Barocas, Hardt, and Narayanan 2019; Mehrabi et al. 2021; S. Mitchell et al. 2021).

Quantifying fairness

We now turn to the question of how we can detect whether disparities exist in a model and if so, how they can be quantified. What constitutes a fair model depends on a society’s ethical values and which normative position we take, resulting in different metrics that are applied to a problem at hand. In this article, we focus on a subgroup of these, so-called statistical group fairness metrics. First, the observations are grouped by a sensitive attribute \(A\) (\(A = 0\) vs. \(A = 1\)), which, e.g., is an identifier for a person’s race or a person’s gender. For the sake of simplicity, we consider a binary classification scenario and a binary sensitive attribute. Each observation has an associated label \(Y, Y \in \{0, 1\}\), and we aim to predict, e.g., whether a defendant was caught re-offending. A system then makes a prediction \(\hat{Y}, \hat{Y} \in \{0,1\}\), with the goal to predict whether an individual might re-offend. We assume that \(Y = 1\) is the favored outcome in the following exposition. While we do not describe them in detail, the concepts discussed in the following often extend naturally to more complex scenarios including multi-class classification, regression or survival analysis. Similarly, metrics can be extended to settings that require consideration of multiple possibly intersecting sensitive attributes. We now provide and discuss groups of metrics that require either Separation or Independence (Barocas, Hardt, and Narayanan 2019) to provide further intuition regarding core concepts and possible applications.

Separation

One group of widely used fairness notions requires Separation: \(\hat{Y} \perp A | Y\). In order for separation to hold, the prediction \(\hat{Y}\) has to be independent of \(A\) given the true label \(Y\). This essentially requires that some notion of model error, e.g., accuracy or false positive rate, is equal across groups \(A\). From this notion, we can derive several metrics that come with different implications. It is important to note that those metrics can only meaningfully identify biases under the assumption that no disparities exist in the data or that they are legally justified. For example, if societal biases lead to disparate measurements of an observed quantity (e.g. SAT scores) for individuals with the same underlying ability, separation based metrics might not identify existing biases. For this reason, Wachter, Mittelstadt, and Russell (2020) refer to those metrics as bias-preserving metrics since underlying disparities are not addressed.

Equalized Odds

A predictor \(\hat{Y}\) satisfies equalized odds with respect to a sensitive attribute \(A\) and observed outcome \(Y\), if \(\hat{Y}\) and \(A\) are conditionally independent given \(Y\): \[\begin{equation} \mathbb{P}\left(\hat{Y} = 1 \mid A = 0, Y = y\right) = \mathbb{P}\left(\hat{Y} = 1 \mid A = 1, Y = y\right), \quad y \in \{0,1\}. (\#eq:eod) \end{equation}\] In short, we require that the true positive rates (TPR) and false positive rates (FPR) across both groups \(A = 0\) and \(A = 1\) are equal. This intuitively requires, e.g., in the case of university admission, independent of the sensitive attribute, equal chances for qualified individuals to be accepted and unqualified individuals to be rejected. Similar measures have been proposed based on equalized false positive rates (Chouldechova 2017) and false omission rates (Berk et al. 2018), depending on the scenario and societal context.

Equality of Opportunity

A predictor \(\hat{Y}\) satisfies equality of opportunity with respect to a sensitive attribute \(A\) and observed outcome \(Y\), if \(\hat{Y}\) and \(A\) are conditionally independent for \(Y = 1\). This is a relaxation of the aforementioned equalized odds essentially only requiring equal TPRs: \[\begin{equation} \mathbb{P}\left(\hat{Y} = 1 \mid A = 0, Y = 1\right) = \mathbb{P}\left(\hat{Y} = 1 \mid A = 1, Y = 1\right). (\#eq:eop) \end{equation}\] Intuitively, this only requires that, independent of the sensitive attribute, qualified individuals have the same chance of being accepted.

Performance Parity

A more general formulation can be applied when we require parity of some performance metric across groups. To provide an example, Buolamwini and Gebru (2018) compare accuracy across intersectional subgroups, essentially arguing that model performance should be equal across groups:

\[\begin{equation} \mathbb{P}\left(\hat{Y} = Y \mid A = 0\right) = \mathbb{P}\left(\hat{Y} = Y \mid A = 1\right). (\#eq:accp) \end{equation}\] This intuitively requires that the model should work equally well for all groups, i.e., individuals are correctly accepted or denied at the same rate, independent of the predicted attribute. This notion can be extended across supervised learning settings and performance metrics, leading to considerations of equal mean squared error, e.g., in a regression setting.

Independence

The second group of fairness metrics is given by so-called bias-transforming metrics (Wachter, Mittelstadt, and Russell 2020). They require that decision rates, such as the positive rate, are equal across groups. This notion can identify biases, e.g., those which arise from societal biases, that manifest in different base rates across groups. At the same time, employing such notions poses a considerable risk, as blindly optimizing for demographic parity might result in predictors that, for example jail innocent people from an advantaged group in order to achieve parity across both groups (Dwork et al. 2012; Berk et al. 2018). A predictor \(\hat{Y}\) satisfies demographic parity (Calders and Verwer 2010) with respect to a sensitive attribute \(A\) and observed outcome \(Y\), if \(\hat{Y}\) and \(A\) are conditionally independent: \[\begin{equation} \mathbb{P}\left(\hat{Y} = 1 \mid A = 0\right) = \mathbb{P}\left(\hat{Y} = 1 \mid A = 1\right). (\#eq:dp) \end{equation}\] In contrast to the previous definitions, this requires that the chance of being accepted is equal across groups.

Fairness metrics

In order to encode the requirements in equations @ref(eq:eod) - @ref(eq:dp) into a fairness metric, we encode differences between measured quantities in two groups. For a performance metric \(M\), e.g., the true positive rate (TPR), we calculate the difference in the metric across the two groups:

\[ \Delta_{\mathrm{M}} = \mathrm{M}_{A=0} - \mathrm{M}_{A=1}. \] When \(\Delta_{\mathrm{M}}\) significantly deviates from \(0\), this indicates a fairness violation with respect to the fairness notion described in \(M\). To provide an example, with \(\mathbb{P}\left(\hat{Y} = 1 \mid A = \star, Y = 1\right)\) denoted with \(\mathrm{TPR}_{A=\star}\), we calculate the difference in TPR between the two groups: \[ \Delta_{\mathrm{TPR}} = \mathrm{TPR}_{A=0} - \mathrm{TPR}_{A=1}. \] When \(\Delta_{\mathrm{TPR}}\) now significantly deviates from \(0\), the prediction \(\hat{Y}\) violates the requirement for equality of opportunity formulated above.

It is important to note that in practice, we might not be able to perfectly satisfy a given metric, e.g., due to stochasticity in data and labels. Instead, to provide a binary conclusion regarding fairness, a model could be considered fair if \(|\Delta_{\mathrm{TPR}}| < \epsilon\) for a given threshold \(\epsilon > 0\), e.g., \(\epsilon = 0.05\). This allows for small deviations from perfect fairness due to variance in the estimation of \(\mathrm{TPR}_{A=\star}\) or additional sources of bias. However, choosing appropriate thresholds is difficult, and widely used values for \(\epsilon\) such as \(0.05\) are arbitrary and do not translate to legal doctrines, e.g., disparate impact (Watkins, McKenna, and Chen 2022). A more in-depth treatment of metrics is given by (Barocas, Hardt, and Narayanan 2019; Saleiro et al. 2018; Kim, Chen, and Talwalkar 2020; Mehrabi et al. 2021; Wachter, Mittelstadt, and Russell 2020).

Selecting fairness metrics

While the aforementioned metrics are conceptually similar, they encode different beliefs of what constitutes fair in a given scenario. Wachter, Mittelstadt, and Russell (2020) differentiate between bias-preserving and bias-transforming metrics: Bias-preserving metrics such as equalized odds and equality of opportunity require that errors made by a model are equal across groups. This can help to detect biases stemming, from imbalances in the sampling or under- and overfitting in ML models, but might be problematic in cases where labels are biased. To provide an example, police enforcement and subsequent arrests of violent re-offenders might be different across ZIP code areas, a proxy for race. This might lead to situations where observed labels \(Y\) suffer from differential measurement bias strongly correlated with race (Bao et al. 2021). Bias-preserving metrics do not take such disparities into account and might, therefore (wrongly) lead to the conclusion that a given model is fair.

Bias-transforming methods, in contrast, do not depend on labels and might therefore not suffer from this problem. They can help detect biases arising from different base rates across populations, arising, e.g., from aforementioned biases in the labelling or as a consequence of structural discrimination. Deciding which metrics to use constitutes a value judgement and requires careful assessment of the societal context a decision-making system is deployed in. A discussion of different metrics and their applicability can be found in the Aequitas Fairness Toolkit (Saleiro et al. 2018) which also provides guidance towards selecting a metric via the Aequitas Fairness Tree. Wachter, Mittelstadt, and Russell (2020) recommend using bias-transforming metrics and provide a checklist that can guide the choice of fairness metric. Corbett-Davies and Goel (2018), on the other hand, point out several limitations of available metrics and argue for grounding decisions in real-world quantities in addition to abstract fairness metrics. Similarly, Friedler, Scheidegger, and Venkatasubramanian (2016) emphasize the need to differentiate between constructs we aim to measure (e.g., job-related knowledge) and the observed quantity that can be measured in practice (e.g., years in a job) when trying to automate decisions, since disparities in how constructs translate to observed quantities might suffer from bias. To provide an example, individuals with similar abilities might exhibit different measured quantities (grades) due to structural bias, e.g., differential access to after-school tutoring programs.

The dangers of fairness metrics

We want to stress that overly trusting in metrics can be dangerous and that fairness metrics cannot and should not be used to prove or guarantee fairness. Whether a selected fairness notion (and a corresponding numerical value) is actually fair depends on the societal context in which a decision is made and which action should be derived from a given prediction. Therefore, selecting the correct fairness metric requires a thorough understanding of the societal context of a decision, as well as the possible implications of such decisions. To provide an example, in some cases discrepancies in positive predictions might be justified or even desired, as they, for example, allow for a more nuanced, gender-specific diagnosis (Cirillo et al. 2020). Furthermore, fairness metrics might not detect biases in more fine-grained subgroups, e.g., at the intersection of multiple sensitive attributes. It is also important to note that fairness metrics merely provide a reduction of the aforementioned fairness notions into mathematical objectives. As such, they require a variety of abstraction steps that might invalidate the metric (Watkins, McKenna, and Chen 2022), as they, for example, require that the data is a large enough and representative sample of exactly the population that we aim to investigate. Furthermore, practitioners need to look beyond the model, and also at the data used for training and the process of data and label acquisition. If the data for example exhibit disparate measurement errors in the features or labels, valid fairness assessments can become impossible. Similarly, feedback loops might arise from a prediction leading to changes in the data collected in the future. Even an initially fair model might then lead to adverse effects in the long term (Schwöbel and Remmers 2022).

Note that the fairness definitions presented above serve a dual purpose (Wachter, Mittelstadt, and Russell 2020): First, as a diagnostic tool to detect disparities. This allows for assessing whether a model has inherited biases, e.g., from historical disparities reflected in the data. The second purpose is as a basis for model selection and making fair decisions in practice. In this setting, fairness notions are employed to audit ML models or to select which model should be used in practice. In this setting, it is important to note that fairness metrics should not be used as the sole basis for making decisions about whether to employ a given ML model or to assess whether a given system is fair. We therefore explicitly encourage using the presented metrics for exploratory purposes.

Other notions of fairness

In addition to statistical group fairness notions introduced above, several additional fairness notions exist. The notion of individual fairness was proposed by Dwork et al. (2012). Its 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 in Binns (2020). Similarly, a variety of causal fairness notions exist (c.f. Kilbertus et al. (2017)). They argue that assessing fairness requires incorporating causal relationships in the data and propose a variety of causal fairness metrics based on a directed acyclic graph describing relationships in the data.

Fairness constraints

Statistical group fairness notions suffer from two further problems in practice: First, it might be hard to exactly satisfy the required fairness notions, e.g., due to a limited amount of data available for evaluation. Secondly, only requiring fairness might lead to degenerate solutions (Corbett-Davies and Goel 2018) or models that have low utility, e.g., in separating good and bad credit risk. One approach to take this into account is to employ models which maximize utility but satisfy some maximum constraint on potential unfairness. This can be achieved via constraints on the employed fairness measure, e.g. \(|\Delta_M| \leq \epsilon\) requiring that the absolute difference in a metric \(M\) between groups is smaller than a chosen value \(\epsilon\). In the following, we denote the fairness metric we want to minimize with \(\Delta_M\) and a performance metric with \(\rho\).

\[ \rho_{|\Delta_M| \leq \epsilon} = \left\{ \begin{array}{ll} \rho & |\Delta_M| \leq \epsilon \\ - |\Delta_M| & \textrm{else.} \\ \end{array} \right. \]

Note that this assumes that the fairness metric \(\rho\) is strictly positive and should be maximized. This approach is similar in spirit to the approach of Perrone et al., (2021) who optimize the constrained expected improvement \(cEI = \mathbb{P}(|\Delta_M| \leq \epsilon) \cdot \rho\).

However, it is not immediately clear how the constraint \(\epsilon\) should be chosen. An alternative, therefore, is to employ multi-objective optimization to investigate available trade-offs between performance and accuracy metrics. This can be done via (Becker, Lang, et al. 2023) which contains functionality to tune models for multiple metrics, described in more detail in the mlr3book (Bernd Bischl 2024). The result of multi-objective optimization then is the Pareto-set: A list of models which optimally trade off the specified objectives.

Bias mitigation

If biases are detected in a model, we might now be interested in improving models in order to potentially mitigate such biases. Bias in models might arise from a variety of sources, so a careful understanding of the data, data quality and distribution might lead to approaches that can help in decreasing biases, e.g. through the collection of better or additional data or a better balancing of sensitive groups. Similarly, biases might arise from the model, through under- or overfitting and more careful tuning of model hyperparameters might help with improving fairness. Especially if the goal is to satisfy bias-transforming metrics, a better solution might often be to address fairness problems in the real world instead of relying on algorithmic interventions to solve fairness. This might lead to more robust, long-term solutions instead of temporarily addressing issues via algorithmic interventions. In addition, a variety of algorithmic bias mitigation techniques, that might help with obtaining fairer models have been proposed. Their goal is to reduce measured gaps in fairness, either via data pre-processing, employing models that incorporate fairness, or by applying post-processing techniques to a model’s predictions. Popular examples of such techniques include computing instance weights before training (Kamiran and Calders 2012), where each observation is weighted proportional to the inverse frequency of its label and sensitive attribute. Other methods work by directly learning fair models that incorporate fairness constraints into the fitting procedure (Zafar et al. 2017) or by adapting model predictions, e.g., Hardt, Price, and Srebro (2016) propose to randomly flip a small fraction of predictions in each group given by \(\hat{Y}\) and \(A\), such that fairness metrics are satisfied in expectation. Since bias mitigation techniques are often tailored towards a particular fairness metric, the optimal choice is often not trivial and a combination of algorithms and bias mitigation techniques determined via tuning might result in an optimal model.

Bias-mitigation techniques, as proposed above, have the goal of mitigating fairness issues, as measured by fairness metrics. In practice, this usually comes with several drawbacks: First, bias-mitigation strategies often lead to a decrease in a classifier’s predictive performance (Corbett-Davies and Goel 2018). In addition, processing schemes can worsen interpretability or introduce stochasticity during prediction (see, e.g., Hardt, Price, and Srebro (2016)). Furthermore, we want to caution against favouring bias-mitigation techniques over policy interventions that tackle biases at their root cause. A different set of risks is posed by fairwashing (Aivodji et al. 2019), i.e., finding fair explanations or satisfying fairness metrics for otherwise unfair models. If biases are only addressed at a given moment and without regard for downstream effects, they might simultaneously lead to a decrease in predictive performance in the near term and to negative consequences for the sensitive group in the long term (Schwöbel and Remmers 2022).

The package

In this section, we first give an overview of related software. Next, we give a very brief introduction to the ecosystem of packages. Finally, the implemented extensions for fairness are presented.

The mlr3 ecosystem

is tightly integrated into the ecosystem of packages around the ML framework (Lang et al. 2019). provides the infrastructure to fit, resample, and evaluate over 100 ML algorithms using a unified API. Packages from the ecosystem can be installed and updated via the (Lang and Schratz 2023) package. Multiple extension packages bring numerous additional advantages and extra functionality. In the context of fairness, the following extension packages deserve special mention:

  • (Binder et al. 2021) for pre- and postprocessing via pipelining. This allows composing bias mitigation techniques with arbitrary ML algorithms shipped with as well as fusing ML algorithms with pre-processing steps such as imputation or class balancing. It furthermore integrates with (Pfisterer et al. 2021), which implements additional bias mitigation methods. We present an example in the supplementary material.
  • and (Becker, Richter, et al. 2023) for its extensive tuning capabilities.
  • (Sonabend et al. 2021) for survival analysis.
  • for post-hoc analysis of benchmarked approaches.
  • as a connector to OpenML (Vanschoren et al. 2014), an online scientific platform for collaborative ML.

In order to provide the required understanding for , we briefly introduce some terminology and syntax. A full introduction can be found in the mlr3 book (Bernd Bischl 2024).

A Task in is a basic building block holding the data, storing covariates and the target variable along with some meta-information. The shorthand constructor function tsk() can be used to quickly access example tasks shipped with or . In the following chunk, we retrieve the binary classification task with id "adult_train" from the package. It contains a part of the Adult data set (Dua and Graff 2017). The task is to predict whether an individual earns more than $50.000 per year. The column "sex" is set as a binary sensitive attribute with levels "Female" and "Male".

library("mlr3verse")
library("mlr3fairness")

task = tsk("adult_train")
print(task)
## 
## ── <TaskClassif> (30718x13) ──────────────────────────────────────────────────────────────────
## • Target: target
## • Target classes: <=50K (positive class, 75%), >50K (25%)
## • Properties: twoclass
## • Features (12):
##   • fct (7): education, marital_status, occupation, race, relationship, sex, workclass
##   • int (5): age, capital_gain, capital_loss, education_num, hours_per_week
## • Protected attribute: sex

The second building block is the Learner. It is a wrapper around an ML algorithm, e.g., an implementation of logistic regression or a decision tree. It can be trained on a Task and used for obtaining a Prediction on an independent test set which can subsequently be scored using a Measure to get an estimate for the predictive performance on new data. The shorthand constructors lrn() and msr() allow for the instantiation of implemented Learners and Measures, respectively. In the following example, we will first instantiate a learner, then split our data into a train and test set, afterwards train it on the train set of the dataset and finally evaluate predictions on held-out test data. The train-test split in this case is given by row indices, here stored in the idx variable.

learner = lrn("classif.rpart", predict_type = "prob")
idx = partition(task)
learner$train(task, idx$train)
prediction = learner$predict(task, idx$test)

We then employ the classif.acc measure which measures the accuracy of a prediction compared to the true label:

measure = msr("classif.acc")
prediction$score(measure)
## classif.acc 
##      0.8443

In the example above, we obtain an accuracy score of 0.8443, meaning our ML model correctly classifies roughly 84 % of the samples in the test data. As the split into training set and test set is stochastic, the procedure should be repeated multiple times for smaller datasets (Bischl et al. 2012) and the resulting performance values should be aggregated. This process is called resampling, and can easily be performed with the resample() function, yielding a ResampleResult object. In the following, we employ 10-fold cross-validation as a resampling strategy:

resampling = rsmp("cv", folds = 10)
rr = resample(task, learner, resampling)
## Warning: package 'future' was built under R version 4.5.2
## Warning: package 'mlr3fselect' was built under R version 4.5.2

We can call the aggregate method on the ResampleResult to obtain the accuracy aggregated across all \(10\) replications.

rr$aggregate(measure)
## classif.acc 
##      0.8408

Here, we obtain an accuracy of 0.8408, so slightly higher than previous scores, due to using a larger fraction of the data. Furthermore, this estimate has a lower variance (as it is an aggregate) at the cost of additional computation time. To properly compare competing modelling approaches, candidates can be benchmarked against each other using the benchmark() function (yielding a BenchmarkResult). In the following, we compare the decision tree from above to a logistic regression model. To do this, we use the benchmark_grid function to compare the two Learners across the same Task and resampling procedure. Finally, we aggregate the measured scores each learner obtains on each cross-validation split using the $aggregate() function.

learner2 = lrn("classif.log_reg", predict_type = "prob")

grid = benchmark_grid(task, list(learner, learner2), resampling)
bmr = benchmark(grid)

bmr$aggregate(measure)[, .(learner_id, classif.acc)]
##         learner_id classif.acc
##             <char>       <num>
## 1:   classif.rpart      0.8408
## 2: classif.log_reg      0.8469

After running the benchmark, we can again call .$aggregate to obtain aggregated scores. The package comes with several ready-made visualizations for objects from mlr3 via ’s (Wickham 2016) autoplot function. For a BenchmarkResult, the autoplot function provides a Box-plot comparison of performances across the cross-validation folds for each Learner. Figure @ref(fig:bmrbox) contains the box-plot comparison. We can see that log_reg has higher accuracy and lower interquartile range across the 10 folds, and we might therefore want to prefer the log_reg model.

Side-by-side comparison of twoboxplots for the rpart and log\_reg learners, both showing a classification accuracy of around .845 and IQR of .005 with log\_reg having slightly higher accuracy.

Model comparison based on accuracy for decision trees (rpart) and logistic regression (log_reg) across resampling splits.

Selecting the sensitive attribute

For a given task, we can select one or multiple sensitive attributes. In , the sensitive attribute is identified by the column role pta and can be set as follows:

task$set_col_roles("marital_status", add_to = "pta")

In the example above, we add the "marital_status" as an additional sensitive attribute. This information is then automatically passed on when the task is used, e.g., when computing fairness metrics. If more than one sensitive attribute is specified, metrics will be computed based on intersecting groups formed by the columns.

Quantifying fairness

With the package loaded, fairness measures can be constructed via msr() like any other measure in . They are listed with prefix fairness, and simply calling msr() without any arguments will return a list of all available measures. Table @ref(tab:metrics) provides an overview of some popular fairness measures which are readily available.

(#tab:metrics) Overview of fairness metrics available with mlr3fairness.
key description
fairness.acc Accuracy equality
fairness.mse Mean squared error equality (Regression)
fairness.eod Equalized odds
fairness.tpr True positive rate equality / Equality of opportunity
fairness.fpr False positive rate equality / Predictive equality
fairness.tnr True negative rate equality
fairness.fnr False negative rate equality
fairness.fomr False omission rate equality
fairness.tnr Negative predictive value equality
fairness.tnr Positive predictive value equality
fairness.cv Demographic parity / Equalized positive rates
fairness.pp Predictive parity / Equalized precision
fairness.{tp, fp, tn, fn} Equal true positives, false positives, true negatives, false negatives
fairness.acc_eod=.05 Accuracy under equalized odds constraint
fairness.acc_ppv=.05 Accuracy under ppv constraint

Furthermore, new custom fairness measures can be easily implemented, either by implementing them directly or by composing them from existing metrics. This process is extensively documented in an accompanying measures vignette available with the package.

Here we choose the binary accuracy measure "classif.acc" and the equalized odds metric from above using "fairness.eod": The constructed list of measures can then be used to score a Prediction, a ResampleResult or BenchmarkResult, e.g.

measures = list(msr("classif.acc"), msr("fairness.eod"))
rr$aggregate(measures)
##             classif.acc fairness.equalized_odds 
##                 0.84078                 0.07837

We can clearly see a comparatively large difference in equalized odds at around 0.08. This means, that in total, the false positive rates (FPR) and true positive rates (TPR) on average differ by ~0.08, indicating that our model might exhibit a bias. Looking at the individual components yields a clearer picture. Here, we are looking at the confusion matrices of the combined predictions of the 10 folds, grouped by sensitive attribute:

fairness_tensor(rr)
## $Male
##         truth
## response   <=50K    >50K
##    <=50K 0.43030 0.10033
##    >50K  0.03408 0.11202
## 
## $Female
##         truth
## response    <=50K     >50K
##    <=50K 0.282668 0.020900
##    >50K  0.003907 0.015789

Plotting the prediction density or comparing measures graphically often provides additional insights: For example, in Figure @ref(fig:predplots), we can see that Females are more often predicted to earn below $50.000. Similarly, we can see that both equality in FPR and TPR differ considerably.

fairness_prediction_density(prediction, task)
compare_metrics(prediction, msrs(c("fairness.fpr", "fairness.tpr", "fairness.eod")), task)
Two pannel plot. On the left hand side, a plot of rediction densities for the negative class for groups Female and Male with density concentrating towards the right. Plots show a higher likelihood for the '<50k' class for females resulting in large fairness metrics. Right: Fairness metrics comparison using bar plots for FPR, TPR, EOd metrics indicating disparities of aroung 0.1 for all metrics.

Visualizing predictions of the decision tree model. Left: Prediction densities for the negative class for Female and Male. Right: Fairness metrics comparison for FPR, TPR, EOd metrics. Plots show a higher likelihood for the ‘<50k’ class for females resulting in fairness metrics different from 0.

Bias mitigation

As mentioned above, several ways to improve a model’s fairness exist. While non-technical interventions, such as e.g. collecting more data should be preferred, provides several bias mitigation techniques that can be used together with a Learner to obtain fairer models. Table @ref(tab:biasmitigation) provides an overview of implemented bias mitigation techniques. They are implemented as PipeOps from the package and can be combined with arbitrary learners using the %>>% operator to build a pipeline that can later be trained. In the following example, we show how to combine a learner with a reweighing scheme (reweighing_wts) or alternatively how to post-process predictions using the equalized odds debiasing (EOd) strategy. An introduction to is available in the corresponding mlr3book chapter (Bernd Bischl 2024).

po("reweighing_wts") %>>% lrn("classif.glmnet")
po("learner_cv", lrn("classif.glmnet")) %>>% po("EOd")
(#tab:biasmitigation) Overview of bias mitigation techniques available in mlr3fairness.
Key Description Type Reference
EOd Equalized-Odds Debiasing Postprocessing Hardt, Price, and Srebro (2016)
reweighing_os Reweighing (Oversampling) Preprocessing Kamiran and Calders (2012)
reweighing_wts Reweighing (Instance weights) Preprocessing Kamiran and Calders (2012)

It is simple for users or package developers to extend with additional bias mitigation methods – as an example, the package adds further post-processing methods that can improve fairness.
Along with pipeline operators, contains several machine learning algorithms listed in table @ref(tab:fairlearns) that can directly incorporate fairness constraints. They can similarly be constructed using the lrn() shorthand.

(#tab:fairlearns) Overview of fair ML algorithms available with mlr3fairness.
Key Package Reference
regr.fairfrrm fairml Scutari, Panero, and Proissl (2021)
classif.fairfgrrm fairml Scutari, Panero, and Proissl (2021)
regr.fairzlm fairml Zafar et al. (2017)
classif.fairzlrm fairml Zafar et al. (2017)
regr.fairnclm fairml Komiyama et al. (2018)

Reports

Because fairness aspects can not always be investigated based on the fairness definitions above (e.g., due to biased sampling or labelling procedures), it is important to document data collection and the resulting data as well as the models resulting from this data. Informing auditors about those aspects of a deployed model can lead to better assessments of a model’s fairness. Questionnaires for ML models (M. Mitchell et al. 2019) and data sets (Gebru et al. 2021) have been proposed in literature. We further add automated report templates using R markdown (Xie, Dervieux, and Riederer 2020) for data sets and ML models. In addition, we provide a template for a fairness report which includes many fairness metrics and visualizations to provide a good starting point for generating a fairness report inspired by the Aequitas Toolkit (Saleiro et al. 2018). A preview for the different reports can be obtained from the Reports vignette in the package documentation.

(#tab:reports) Overview of reports generated by mlr3fairness.
Report Description Reference
report_modelcard() Modelcard for ML models M. Mitchell et al. (2019)
report_datasheet() Datasheet for data sets Gebru et al. (2021)
report_fairness() Fairness Report –

Case study

In order to demonstrate a full workflow, we conduct full bias assessment and bias mitigation on the popular adult data set (Dua and Graff 2017). The goal is to predict whether an individual’s income is larger than $\(50.000\) with the sensitive attribute being gender. The data set is included with , separated into a train and test task and can be instantiated using tsk("adult_train") and tsk("adult_test"), respectively. As a fairness metric, we consider true positive parity which calls for equality in the true positive rates across groups, in this case the sex variable. We furthermore are interested in the model’s utility, here measured as its classification accuracy.

library("mlr3verse")
library("mlr3fairness")

task = tsk("adult_train")
print(task)
## 
## ── <TaskClassif> (30718x13) ──────────────────────────────────────────────────────────────────
## • Target: target
## • Target classes: <=50K (positive class, 75%), >50K (25%)
## • Properties: twoclass
## • Features (12):
##   • fct (7): education, marital_status, occupation, race, relationship, sex, workclass
##   • int (5): age, capital_gain, capital_loss, education_num, hours_per_week
## • Protected attribute: sex
measures = msrs(c("fairness.tpr", "classif.acc"))

In order to get an initial perspective, we benchmark three models using 3-fold cross-validation each:

The logistic regression in the latter two approaches does not support operating on factor features natively, therefore we pre-process the data with a feature encoder from . To achieve this, we connect the feature encoder po("encode") with the learner using the %>>% operator. This encodes factor variables into integers using dummy encoding. We then evaluate all three learners on the adult_train data using 3-fold cross-validation by building up a grid of experiments we want to run using benchmark_grid. This grid is then executed using the benchmark function, and we can aggregate the performance and fairness metric scores via the $aggregate() function.

set.seed(4321)
learners = list(
    lrn("classif.rpart"),
    po("encode") %>>% lrn("classif.glmnet"),
    po("encode") %>>% po("reweighing_wts") %>>% lrn("classif.glmnet")
)

grid = benchmark_grid(
  tasks = tsks("adult_train"),
  learners = learners,
  resamplings = rsmp("cv", folds = 3)
)

bmr1 = benchmark(grid)
bmr1$aggregate(measures)[, c(4, 7, 8)]
##                              learner_id fairness.tpr classif.acc
##                                  <char>        <num>       <num>
## 1:                        classif.rpart      0.05978      0.8408
## 2:                encode.classif.glmnet      0.07030      0.8416
## 3: encode.reweighing_wts.classif.glmnet      0.00380      0.8352

The pre-processing step of reweighing already improved the fairness while sacrificing only a tiny bit of performance. To see if we can further improve, we use to jointly tune all hyperparameters of the glmnet model as well as our reweighing hyperparameter. In order to do this, we use an AutoTuner from ; a model that tunes its own hyperparameters during training. The full code for setting up this model can be found in the appendix. An AutoTuner requires a specific metric to tune for. Here, we define a fairness-thresholded accuracy metric. We set \(\epsilon = 0.01\) as a threshold:

\[ if \; |\Delta_{EOd}| \leq \epsilon: \textrm{accuracy} \;\; else: \; - |\Delta_{EOd}|. \]

metric = msr("fairness.constraint",
    performance_measure = msr("classif.acc"),
    fairness_measure = msr("fairness.eod"),
    epsilon = 0.01
)

We then design the pipeline and the hyperparameters we want to tune over. In the following example, we choose tuning_iters = 3 and set a small range for the hyperparameters in vals to shorten the run time of the tuning procedure. In real settings, this parameter would be set to a larger number, such as \(100\). To construct a self-tuning learner, we construct an AutoTuner that takes as input a learner, the resampling procedure and metric used for tuning as well as the tuning strategy along with a termination criterion (here how many tuning iterations should be run). In addition, we provide a new id for the learner to beautify subsequent printing and visualization. We can then use this self-tuning learner like any other learner and benchmark it using benchmark as described above.

tuning_iters = 3
at = AutoTuner$new(lrn, rsmp("holdout"),
    metric,
    tuner = mlr3tuning::tnr("random_search"),
    terminator = trm("evals", n_evals = tuning_iters)
)
at$id = "glmnet_weighted_tuned"

grd = benchmark_grid(
  tasks = tsks("adult_train"),
  learners = list(at),
  resamplings = rsmp("cv", folds = 3)
)

bmr2 = benchmark(grd, store_models = TRUE)
bmr2$aggregate(measures)[, c(4, 7, 8)]
##               learner_id fairness.tpr classif.acc
##                   <char>        <num>       <num>
## 1: glmnet_weighted_tuned      0.01196      0.8416

The result improves w.r.t. accuracy while only slightly decreasing the measured fairness. Note that the generalization error is estimated using a holdout strategy during training and slight violations of the desired threshold \(\epsilon\) should therefore be considered (Feurer et al. 2023). The results of both benchmark experiments can then be collected and jointly visualized in Figure @ref(fig:fat) visualizing accuracy and fairness of models in our benchmark. In addition to aggregate scores (denoted by a cross) individual iterations of the 3-fold Cross-Validation (represented by points) are shown to visualize variations in the individual results.

Fairness-Accuracy tradeoff for 3-fold CV on the adult train set.

Fairness-Accuracy tradeoff for 3-fold CV on the adult train set. 

bmr$aggregate(measures)[, c(4, 7, 8)]
##                              learner_id fairness.tpr classif.acc
##                                  <char>        <num>       <num>
## 1:                        classif.rpart      0.05978      0.8408
## 2:                encode.classif.glmnet      0.07030      0.8416
## 3: encode.reweighing_wts.classif.glmnet      0.00380      0.8352
## 4:                glmnet_weighted_tuned      0.01196      0.8416

Especially when considering optimizing accuracy while still retaining a fair model, tuning can be helpful and further improve upon available trade-offs. In this example, the AutoTuner improves w.r.t. the fairness metric while offering accuracy comparable with the simple glmnet model. This can be observed from the fairness accuracy tradeoff shown in Figure @ref(fig:fat). Whether the achieved accuracy is sufficient needs to be determined, e.g. from a business context. For now, we assume that the model obtained from the AutoTuner is the model we might want to use going forward. Having decided on a final model, we can now train the final model on the full training data

at_lrn = bmr$learners$learner[[4]]
at_lrn$train(tsk("adult_train"))

and predict on the held out test set available for the Adult dataset to obtain a final estimate. This is important since estimating fairness metrics often incurs significant variance (Agrawal, Pfisterer, et al. 2020) and evaluation of the test-set provides us with an unbiased estimate of model performance after the previous model selection step.

test = tsk("adult_test")
at_lrn$predict(test)$score(measures, test)
## fairness.tpr  classif.acc 
##      0.01102      0.84003

On the held-out test set, the fairness constraint is slightly violated which can happen due to the comparatively large variance in the estimation of fairness metrics.

Summary

The large-scale availability and use of automated decision making systems have resulted in growing concerns for a lack of fairness in the decisions made by such systems. As a result, fairness auditing methods, that allow for investigating (un-)fairness in such systems are an important step towards improving the auditability of deployed systems. For ease of use, it is especially important, that they provide interoperability with machine learning toolkits that allows for ease of use and integration into model evaluation and tuning. In future work we plan on implementing several tools that further support the user w.r.t. pinpointing potential fairness issues in the data, especially through the help of interpretability tools, such as the (Molnar, Bischl, and Casalicchio 2018) package. We furthermore aim to implement additional fairness metrics from the realm of `individual fairness’ (Dwork et al. 2012) and `conditional demographic parity’ (Wachter, Mittelstadt, and Russell 2020).

Appendix

Tuning the ML pipeline

We include the full code to construct the AutoTuner with additional details and comments below. We first load all required packages and use ’s interaction with the (Bengtsson 2021) package to automatically distribute the tuning to all available cores in parallel by setting a plan. See the documentation of for platform-specific hints regarding parallelization.

library(mlr3misc)
library(mlr3)
library(mlr3pipelines)
library(mlr3fairness)
library(mlr3tuning)

# Enable parallelization utilizing all cores
future::plan("multicore")

We then instantiate an ML pipeline using . This connects several modelling steps, in our case categorical encoding, reweighing and a final learner using the %>>% (double caret) operator, ultimately forming a new learner. This learner can then subsequently be fit on a Task. We use the po(<key>) shorthand to construct a new pipeline operator from a dictionary of implemented operators. We conduct categorical encoding because can not naturally handle categorical variables, and we therefore have to encode them (in our case using one-hot encoding).

# Define the learner pipeline.
lrn = as_learner(po("encode") %>>% po("reweighing_wts") %>>%
  po("learner", lrn("classif.glmnet")))

In addition, we have to specify the hyperparameter space our Tuner should tune over. We do this by defining a list of values with a to_tune() token specifying the range. Note, that hyperparameter names are prefixed with the respective operation’s id.

# Define the parameter space to optimize over
vals = list(
  reweighing_wts.alpha = to_tune(0.75, 1),
  classif.glmnet.alpha = to_tune(0.5, 1),
  classif.glmnet.s = to_tune(1e-4, 1e-2, logscale = TRUE)
)

# Add search space to the learner
lrn$param_set$values = insert_named(lrn$param_set$values, vals)

Before we now train the model, we again specify a metric we aim to satisfy, here we would like the equalized odds difference to be smaller than \(0.1\). In this case, we set a constraint on the equalized odds difference comprised of the differences in true positive rate (TPR) and false positive rate (FPR):

\[ \Delta_{EOd} = \frac{|\textrm{TPR}_{sex = M} - \textrm{TPR}_{sex = F}| + |\textrm{FPR}_{sex = M} - \textrm{FPR}_{sex = F}|}{2}. \]

This can be done using the fairness.constraint measure.

metric = msr("fairness.constraint",
    performance_measure = msr("classif.acc"),
    fairness_measure = msr("fairness.eod"),
    epsilon = 0.1
)

We can now instantiate a new AutoTuner using lrn defined above by additionally providing arguments specifying the tuning strategy, in our case random search, the measure to optimize for as well as the number of tuning steps.

metric = msr("fairness.constraint",
    performance_measure = msr("classif.acc"),
    fairness_measure = msr("fairness.eod"),
    epsilon = 0.1
)

at = AutoTuner$new(
  learner = lrn, # The learner
  resampling = rsmp("holdout"), # inner resampling strategy
  measure = metric, # the metric to optimize for
  tuner = mlr3tuning::tnr("random_search"), # tuning strategy
  terminator = trm("evals", n_evals = 30) # number of tuning steps
)

The so-constructed AutoTuner can now be used on any classification Task! Additional information regarding the AutoTuner is again available in the corresponding mlr3book chapter. In the following example, we will apply it to the Adult task and train our model. This will perform a tuning loop for the specified number of evaluations and automatically retrain the best found parameters on the full data.

at$train(tsk("adult_train"))

After training, we can look at the best models found, here ordered by our metric. Note, that our metric reports the negative constraint violation if the constraint is violated and the accuracy in case the constraint is satisfied.

head(at$archive$data[order(fairness.acc_equalized_odds_cstrt), 1:4])
##    reweighing_wts.alpha classif.glmnet.alpha classif.glmnet.s
##                   <num>                <num>            <num>
## 1:               0.8339               0.9952           -5.052
## 2:               0.9315               0.5919           -4.915
## 3:               0.7548               0.5857           -4.928
## 4:               0.9665               0.5841           -6.777
## 5:               0.8019               0.5946           -6.950
## 6:               0.7703               0.5352           -5.331
##    fairness.acc_equalized_odds_cstrt
##                                <num>
## 1:                            0.8387
## 2:                            0.8392
## 3:                            0.8394
## 4:                            0.8409
## 5:                            0.8411
## 6:                            0.8412

We can then use the tuned model to assess our metric on the held out data:

prd = at$predict(tsk("adult_test"))
prd$score(c(metric, msr("classif.acc"), msr("fairness.eod")),  tsk("adult_test"))
## fairness.acc_equalized_odds_cstrt                       classif.acc 
##                           0.83924                           0.83924 
##           fairness.equalized_odds 
##                           0.07596

So our tuned model manages to obtain an accuracy of ~0.84 while satisfying the specified constraint of \(\Delta_{EOd} < 0.1\). So to summarize, we have tuned a model to optimize accuracy with respect to a constraint on a selected fairness metric using an AutoTuner.

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