Fairness in Clustering with Multiple Sensitive Attributes
This work addresses fairness in clustering for scenarios with multiple sensitive attributes, which is an incremental advancement in the field.
The paper tackles fair clustering with multiple sensitive attributes by proposing FairKM, a method that improves both clustering quality and fairness compared to a state-of-the-art baseline, as shown in empirical evaluations on real-world datasets.
A clustering may be considered as fair on pre-specified sensitive attributes if the proportions of sensitive attribute groups in each cluster reflect that in the dataset. In this paper, we consider the task of fair clustering for scenarios involving multiple multi-valued or numeric sensitive attributes. We propose a fair clustering method, \textit{FairKM} (Fair K-Means), that is inspired by the popular K-Means clustering formulation. We outline a computational notion of fairness which is used along with a cluster coherence objective, to yield the FairKM clustering method. We empirically evaluate our approach, wherein we quantify both the quality and fairness of clusters, over real-world datasets. Our experimental evaluation illustrates that the clusters generated by FairKM fare significantly better on both clustering quality and fair representation of sensitive attribute groups compared to the clusters from a state-of-the-art baseline fair clustering method.