We consider the problem of deep fair clustering, which partitions data into
clusters via the representations extracted by deep neural networks while hiding
sensitive data attributes. To achieve fairness, existing methods present a
variety of fairness-related objective functions based on the group fairness
criterion. However, these works typically assume that the sensitive attributes
are discrete and do not work for continuous sensitive variables, such as the
proportion of the female population in an area. Besides, the potential of the
representations learned from clustering tasks to improve performance on other
tasks is ignored by existing works. In light of these limitations, we propose a
flexible deep fair clustering method that can handle discrete and continuous
sensitive attributes simultaneously. Specifically, we design an information
bottleneck style objective function to learn fair and clustering-friendly
representations. Furthermore, we explore for the first time the transferability
of the extracted representations to other downstream tasks. Unlike existing
works, we impose fairness at the representation level, which could guarantee
fairness for the transferred task regardless of clustering results. To verify
the effectiveness of the proposed method, we perform extensive experiments on
datasets with discrete and continuous sensitive attributes, demonstrating the
advantage of our method in comparison with state-of-the-art methods.

我们提出了一种灵活的深度公平聚类方法，可以同时处理离散和连续的敏感属性，通过设计一个信息瓶颈风格的目标函数来学习公平和友好的聚类表示，并首次探索了从聚类任务中提取的表示的可转移性到其他下游任务，实验证明我们的方法相比现有方法具有优势。

从离散到连续：具有可传递表示的深度公平聚类

From Discrete to Continuous: Deep Fair Clustering With Transferable  Representations

Fair clustering under the disparate impact doctrine requires that population
of each protected group should be approximately equal in every cluster.
Previous work investigated a difficult-to-scale pre-processing step for
$k$-center and $k$-median style algorithms for the special case of this problem
when the number of protected groups is two. In this work, we consider a more
general and practical setting where there can be many protected groups. To this
end, we propose Deep Fair Clustering, which learns a discriminative but fair
cluster assignment function. The experimental results on three public datasets
with different types of protected attribute show that our approach can steadily
improve the degree of fairness while only having minor loss in terms of
clustering quality.

本论文研究了不同群体下公平聚类问题。作者提出了一种基于深度学习的算法 Deep Fair Clustering 来实现公平聚类，实验结果表明，该方法在保证聚类质量的基础上，大幅提高了公平性水平。