Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of machine learning with far-reaching societal impact. However, existing fair learning methods are vulnerable to accidental or malicious artifacts in the training data, which can cause them to unknowingly produce unfair classifiers. In this work we address the problem of fair learning from unreliable training data in the robust multisource setting, where the available training data comes from multiple sources, a fraction of which might not be representative of the true data distribution. We introduce FLEA, a filtering-based algorithm that allows the learning system to identify and suppress those data sources that would have a negative impact on fairness or accuracy if they were used for training. We show the effectiveness of our approach by a diverse range of experiments on multiple datasets. Additionally, we prove formally that - given enough data - FLEA protects the learner against corruptions as long as the fraction of affected data sources is less than half.

翻译：公平了解学习旨在构建不仅作出准确预测,而且不歧视特定群体的分类师,这是一个快速增长的机器学习领域,具有深远的社会影响。然而,现有的公平学习方法在培训数据中容易发生意外或恶意文物,从而导致他们不知情地产生不公平的分类师。在这项工作中,我们处理从强有力的多来源环境中不可靠的培训数据中公平学习的问题,因为现有培训数据来自多个来源,其中一小部分可能无法代表真实的数据分布。我们引入了基于过滤的算法,使学习系统能够识别和抑制那些如果用于培训会对公平性或准确性产生消极影响的数据源。我们通过多种数据集的多种实验展示了我们的方法的有效性。此外,我们正式证明,只要受影响的数据源的比例不到一半,只要有了足够的数据,FLEA就能保护学习者免受腐败。