Fair Learning with a Small Number of Sensitive Attributes

Abstract

With the recent development of machine learning technology, machine learning-based decision-making has been used in various situations. However, some researchers have pointed out the unfairness of machine learning. Most existing studies on fairness assume that all the data have sensitive attributes. However, in many situations, we cannot observe the sensitive attributes because laws and regulations may prevent the collection of such data, and also the annotation cost is high. In this study, we develop fair classification algorithms for situations where only a small number of sensitive attributes are available. Our algorithm creates a sensitive attribute classifier using a semi-supervised learning method and assigns the output of a classifier to unlabelled data as a pseudo-sensitive attribute. As a result, we can execute a existing fairness method using pseudo-sensitive attributes. Experimental results show that the proposed method can achieve a competitive trade-off between accuracy and fairness compared to the ideal case where all the data have sensitive attributes, even though our method can access only a few labels of sensitive attributes. In addition, we found that the accuracy of the pseudo-sensitive attributes is essential to achieve fairness. Also, we found that filtering by confidence could negatively affect the accuracy.