When it is ethical and legal to use a sensitive attribute (such as gender or
race) in machine learning systems, the question remains how to do so. We show
that the naive application of machine learning algorithms using sensitive
features leads to an inherent tradeoff in accuracy between groups. We provide a
simple and efficient decoupling technique, that can be added on top of any
black-box machine learning algorithm, to learn different classifiers for
different groups. Transfer learning is used to mitigate the problem of having
too little data on any one group.
The method can apply to a range of fairness criteria. In particular, we
require the application designer to specify as joint loss function that makes
explicit the trade-off between fairness and accuracy. Our reduction is shown to
efficiently find the minimum loss as long as the objective has a certain
natural monotonicity property which may be of independent interest in the study
of fairness in algorithms.

本文研究当使用敏感信息来训练机器学习系统时如何保证公平性，提出了一种有效的技术 —— 基于转移学习的去耦合方法，并且可适用于多个公平标准下的任务，但是需要在应用设计者规定的联合损失函数下权衡公平性和准确性。