一句话：提出“反保护属性”概念，用网格搜索表明机器学习软件可在去偏的同时保留对反保护属性的区分能力。
机器学习已成为众多现代决策软件的核心，但其可能带有偏见，导致对个人或群体的不公对待。常见的公平性方法要求无论敏感属性（如性别、种族）如何，个体应获得同等收益（例如学生得到相同成绩）。然而，某些属性恰恰应当被区分对待，例如“是否完成作业”的学生应获得更高成绩，作者将这类属性称为反保护属性。去偏过程可能削弱机器学习软件对反保护属性的区分能力，从而带来新的风险。为应对这一问题，作者采用网格搜索方法进行实验，结果表明机器学习软件可以在降低偏见（如减少性别偏见）的同时，提升对反保护属性的区分能力，从而在公平性与合理区分之间取得平衡。
Machine Learning is a vital part of various modern day decision making software. At the same time, it has shown to exhibit bias, which can cause an unjust treatment of individuals and population groups. One method to achieve fairness in machine learning software is to provide individuals with the same degree of benefit, regardless of sensitive attributes (e.g., students receive the same grade, independent of their sex or race). However, there can be other attributes that one might want to discriminate against (e.g., students with homework should receive higher grades). We will call such attributes anti-protected attributes. When reducing the bias of machine learning software, one risks the loss of discriminatory behaviour of anti-protected attributes. To combat this, we use grid search to show that machine learning software can be debiased (e.g., reduce gender bias) while also improving the ability to discriminate against anti-protected attributes.