  Clustering algorithms are widely used in many societal resource allocation
applications, such as loan approvals and candidate recruitment, among others,
and hence, biased or unfair model outputs can adversely impact individuals that
rely on these applications. To this end, many fair clustering approaches have
been recently proposed to counteract this issue. Due to the potential for
significant harm, it is essential to ensure that fair clustering algorithms
provide consistently fair outputs even under adversarial influence. However,
fair clustering algorithms have not been studied from an adversarial attack
perspective. In contrast to previous research, we seek to bridge this gap and
conduct a robustness analysis against fair clustering by proposing a novel
black-box fairness attack. Through comprehensive experiments, we find that
state-of-the-art models are highly susceptible to our attack as it can reduce
their fairness performance significantly. Finally, we propose Consensus Fair
Clustering (CFC), the first robust fair clustering approach that transforms
consensus clustering into a fair graph partitioning problem, and iteratively
learns to generate fair cluster outputs. Experimentally, we observe that CFC is
highly robust to the proposed attack and is thus a truly robust fair clustering
alternative.
