There have been many papers with algorithms for improving fairness of machine-learning classifiers for tabular data. Unfortunately, most use only very few datasets for their experimental evaluation. We introduce a suite of functions for fetching 20 fairness datasets and providing associated fairness metadata. Hopefully, these will lead to more rigorous experimental evaluations in future fairness-aware machine learning research.

翻译：已有大量论文提出了用于提升表格数据机器学习分类器公平性的算法。然而，大多数算法在实验评估中仅使用了极少量数据集。本文引入一套函数工具集，可获取20个公平性数据集并提供相应的公平性元数据。希望这些资源能够推动未来公平感知机器学习研究中更加严谨的实验评估。