The right to be forgotten (RTBF) is motivated by the desire of people not to be perpetually disadvantaged by their past deeds. For this, data deletion needs to be deep and permanent, and should be removed from machine learning models. Researchers have proposed machine unlearning algorithms which aim to erase specific data from trained models more efficiently. However, these methods modify how data is fed into the model and how training is done, which may subsequently compromise AI ethics from the fairness perspective. To help software engineers make responsible decisions when adopting these unlearning methods, we present the first study on machine unlearning methods to reveal their fairness implications. We designed and conducted experiments on two typical machine unlearning methods (SISA and AmnesiacML) along with a retraining method (ORTR) as baseline using three fairness datasets under three different deletion strategies. Experimental results show that under non-uniform data deletion, SISA leads to better fairness compared with ORTR and AmnesiacML, while initial training and uniform data deletion do not necessarily affect the fairness of all three methods. These findings have exposed an important research problem in software engineering, and can help practitioners better understand the potential trade-offs on fairness when considering solutions for RTBF.

翻译：被遗忘权（RTBF）源于人们不希望因过去的行为而长期处于不利地位的诉求。为此，数据删除必须是彻底且永久性的，并且应从机器学习模型中移除。研究人员提出了机器遗忘算法，旨在更高效地从已训练模型中擦除特定数据。然而，这些方法改变了数据输入模型的方式及训练过程，可能会从公平性角度损害人工智能伦理。为了帮助软件工程师在采用这些遗忘方法时做出负责任的决策，本文首次对机器遗忘方法展开研究，以揭示其公平性影响。我们设计并开展实验，针对两种典型的机器遗忘方法（SISA和AmnesiacML），以及作为基准的重新训练方法（ORTR），使用三种公平性数据集在三种不同删除策略下进行测试。实验结果表明，在非均匀数据删除条件下，与ORTR和AmnesiacML相比，SISA能带来更好的公平性；而初始训练与均匀数据删除不一定影响这三种方法的公平性。这些发现揭示了软件工程中的一个重要研究问题，并有助于从业者更好地理解在考虑被遗忘权解决方案时可能面临的公平性权衡。