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<h1 id="introduction-fairness-pipeline-operators">Introduction: Fairness Pipeline Operators</h1>
<p>Given we detected some form of bias during bias auditing, we are often interested in obtaining fair(er) models.
There are several ways to achieve this, such as collecting additional data or finding and fixing errors in the data.
Assuming there are no biases in the data and labels, one other option is to debias models using either <strong>preprocessing</strong>, <strong>postprocessing</strong> and <strong>inprocessing</strong> methods.
<code>mlr3fairness</code> provides some operators as <code>PipeOp</code>s for <code>mlr3pipelines</code>.
If you are not familiar with <code>mlr3pipelines</code>, the <a href="https://mlr3book.mlr-org.com/pipelines.html">mlr3 book</a> contains an introduction.</p>
<p>We again showcase debiasing using the <code>adult_train</code> task:</p>
<pre><code class="language-r">library(mlr3)
library(mlr3fairness)
librar [... truncated]
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<h1 id="fairness-measures">Fairness Measures</h1>
<p>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:</p>
<ul>
<li>
<p><strong>Statistical Group Fairness Metrics</strong>: Given a set of predictions from our model, we assess for differences in one or multiple metrics across groups given by a <em>protected attribute</em> [@fairmlbook; @hardt2016equality].</p>
</li>
<li>
<p><strong>Individual Fairness</strong>: Basically requires that similar people are treated similar independent of the protected attribute [@dwork2012].
We will briefly introduce individual fairness in a dedicated section below.</p>
</li>
<li>
<p><strong>Causal Fairness Notions</strong>: An important realization in the context of Fairness is, that whether a process is fair is o [... truncated]
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<pre><code class="language-r">library(mlr3)
library(mlr3fairness)
library(mlr3learners)
</code></pre>
<h1 id="why-we-need-fairness-visualizations">Why we need fairness visualizations:</h1>
<p>Through fairness visualizations allow for first investigations into possible fairness problems in a dataset.
In this vignette we will showcase some of the pre-built fairness visualization functions.
All the methods showcased below can be used together with objects of type <code>BenchmarkResult</code>, <code>ResampleResult</code> and <code>Prediction</code>.</p>
<h1 id="the-scenario">The scenario</h1>
<p>For this example, we use the <code>adult_train</code> dataset.
Keep in mind all the datasets from <code>mlr3fairness</code> package already set protected attribute via the <code>col_role</code> “pta”, here the “sex” column.</p>
<pre><code class="language-r">t = tsk(&quot;adult_train&quot;)
t$col_roles$pta
#&gt; [1] &quot;sex&quot;
</code></pre [... truncated]
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