As decision-making increasingly relies on Machine Learning (ML) and (big) data, the issue of fairness in data-driven Artificial Intelligence (AI) systems is receiving increasing attention from both research and industry. A large variety of fairness-aware machine learning solutions have been proposed which involve fairness-related interventions in the data, learning algorithms and/or model outputs. However, a vital part of proposing new approaches is evaluating them empirically on benchmark datasets that represent realistic and diverse settings. Therefore, in this paper, we overview real-world datasets used for fairness-aware machine learning. We focus on tabular data as the most common data representation for fairness-aware machine learning. We start our analysis by identifying relationships between the different attributes, particularly w.r.t. protected attributes and class attribute, using a Bayesian network. For a deeper understanding of bias in the datasets, we investigate the interesting relationships using exploratory analysis.
翻译:由于决策日益依赖机器学习和(大)数据,数据驱动人工智能系统的公平问题正日益受到研究和行业的注意,提出了各种公平意识机器学习解决方案,涉及数据、学习算法和(或)模型产出中的公平干预。然而,提出新办法的一个重要部分是以实事求是的方式,根据反映现实和不同环境的基准数据集对其进行实证评估。因此,在本文件中,我们概述了用于公平意识机器学习的真实世界数据集。我们注重表格数据,将其作为公平意识机器学习的最常见数据代表。我们开始进行分析,方法是利用一个Bayesian网络,查明不同属性特别是w.r.t.受保护属性和阶级属性之间的关系。为了更深入地了解数据集中的偏向性,我们利用探索性分析来调查有趣的关系。