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.

翻译：受欧盟现行《人工智能法案》修正案中第67条陈述的启发，我们针对表格数据集中的歧视问题提出并呈现了度量方法与缓解策略。我们特别关注包含多个保护属性（如国籍、年龄和性别）的数据集。这使得偏差的度量和缓解更具挑战性，因为许多现有方法仅针对单一保护属性设计。本文作出双重贡献：首先，引入了新的歧视度量指标。这些指标与现有指标一同在我们的框架中被分类，以指导研究人员和实践者选择合适的度量方法来评估基础数据集的公平性。其次，提出了一种现有偏差缓解方法FairDo的新颖应用。我们证明该策略能够通过转换数据集来缓解任何类型的歧视，包括交叉歧视。通过在真实数据集（Adult、Bank、Compas）上进行实验，我们证明了对于具有多个保护属性的数据集进行去偏是可行的。此外，与原始数据集相比，在这些转换后的公平数据集上训练时，所有测试的机器学习模型的性能均未受到显著影响。在我们的实验中，歧视最多减少了83%。在大多数实验中，受保护群体间的差异平均至少减少了7%，平均减少幅度为27%。总体而言，研究结果表明所采用的缓解策略是有效的，本研究为欧盟《人工智能法案》实施的相关持续讨论作出了贡献。