From Discrete to Continuous: Deep Fair Clustering With Transferable
  Representations
Xiang Zhang
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.