Benchmark datasets to study fairness in synthetic data generation

The traveltime dataset is based on the Folktables project covering US census data. The target is a binary variable encoding whether or not the individual needs to travel more than 20 minutes for work; here, having a shorter travel time is the desirable outcome. We use a subset of data from the states of California, Florida, Maine, New York, Utah, and Wyoming states in 2018. Although the folktables dataset does not have any missing values, there are some values recorded as NaN due to the Bureau's data collection methodology. We remove the "esp" column, which encodes the employment status of parents, and has 99.55% missing values. We encode the missing values in the povpip, income to poverty ratio (0.85%), to -1 in accordance to the methodology in Ding et al.. See https://arxiv.org/pdf/2108.04884 for metadata. The cardio (a) dataset contains patient data recorded during medical examination, including 3 binary features supplied by the patient. The target class denotes the presence of cardiovascular disease. This dataset represents predictive tasks that allocate access to priority medical care for patients, and has been used for fairness evaluations in the domain. The credit dataset contains historical financial data of borrowers, including past non-serious delinquencies. Here, a serious delinquency is considered to be 90 days past due, and this is the target variable. The German Credit dataset (https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data) contains financial and personal information regarding loan-seeking applicants.出行时间数据集（traveltime dataset）基于覆盖美国人口普查数据的Folktables项目构建。其目标变量为二分类变量，用于编码个体是否需要花费超过20分钟通勤上班；其中，通勤时长更短为理想结果。我们使用了2018年加利福尼亚州、佛罗里达州、缅因州、纽约州、犹他州以及怀俄明州的部分数据。尽管Folktables数据集本身不存在缺失值，但由于美国人口普查局的数据收集方法，部分字段被记录为NaN（非数值）。我们移除了“esp”列——该列用于编码父母的就业状态，其缺失值占比高达99.55%。针对贫困线收入比（povpip，income to poverty ratio）字段中占比0.85%的缺失值，我们参照Ding等人的研究方法将其编码为-1。元数据可参见https://arxiv.org/pdf/2108.04884。 心血管疾病（cardio (a)）数据集包含体检期间记录的患者数据，其中包含3项由患者提供的二分类特征。其目标类别用于标识是否存在心血管疾病。该数据集对应面向患者的优先医疗资源分配预测任务，且已被用于该领域的公平性评估研究。 信贷数据集包含借贷者的历史金融数据，其中包含过往非严重逾期记录。此处，严重逾期被定义为逾期90天以上，该指标即为目标变量。 德国信贷数据集（German Credit dataset，https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data）包含贷款申请人的金融与个人信息。