  Clustering algorithms are ubiquitous in modern data science pipelines, and
are utilized in numerous fields ranging from biology to facility location. Due
to their widespread use, especially in societal resource allocation problems,
recent research has aimed at making clustering algorithms fair, with great
success. Furthermore, it has also been shown that clustering algorithms, much
like other machine learning algorithms, are susceptible to adversarial attacks
where a malicious entity seeks to subvert the performance of the learning
algorithm. However, despite these known vulnerabilities, there has been no
research undertaken that investigates fairness degrading adversarial attacks
for clustering. We seek to bridge this gap by formulating a generalized attack
optimization problem aimed at worsening the group-level fairness of
centroid-based clustering algorithms. As a first step, we propose a fairness
degrading attack algorithm for k-median clustering that operates under a
whitebox threat model -- where the clustering algorithm, fairness notion, and
the input dataset are known to the adversary. We provide empirical results as
well as theoretical analysis for our simple attack algorithm, and find that the
addition of the generated adversarial samples can lead to significantly lower
fairness values. In this manner, we aim to motivate fairness degrading
adversarial attacks as a direction for future research in fair clustering.
