  Motivated by the recital (67) of the current corrigendum of the AI Act in the
European Union, we propose and present measures and mitigation strategies for
discrimination in tabular datasets. We specifically focus on datasets that
contain multiple protected attributes, such as nationality, age, and sex. This
makes measuring and mitigating bias more challenging, as many existing methods
are designed for a single protected attribute. This paper comes with a twofold
contribution: Firstly, new discrimination measures are introduced. These
measures are categorized in our framework along with existing ones, guiding
researchers and practitioners in choosing the right measure to assess the
fairness of the underlying dataset. Secondly, a novel application of an
existing bias mitigation method, FairDo, is presented. We show that this
strategy can mitigate any type of discrimination, including intersectional
discrimination, by transforming the dataset. By conducting experiments on
real-world datasets (Adult, Bank, Compas), we demonstrate that de-biasing
datasets with multiple protected attributes is achievable. Further, the
transformed fair datasets do not compromise any of the tested machine learning
models' performances significantly when trained on these datasets compared to
the original datasets. Discrimination was reduced by up to 83% in our
experimentation. For most experiments, the disparity between protected groups
was reduced by at least 7% and 27% on average. Generally, the findings show
that the mitigation strategy used is effective, and this study contributes to
the ongoing discussion on the implementation of the European Union's AI Act.
