When Correlation Clustering Meets Fairness Constraints

The study of fairness-related aspects in data analysis is an active field of research, which can be leveraged to understand and control specific types of bias in decision-making systems. A major problem in this context is fair clustering, i.e., grouping data objects that are similar according to a common feature space, while avoiding biasing the clusters against or towards particular types of classes or sensitive features. In this work, we focus on a correlation-clustering method we recently introduced, and experimentally assess its performance in a fairness-aware context. We compare it to state-of-the-art fair-clustering approaches, both in terms of classic clustering quality measures and fairness-related aspects. Experimental evidence on public real datasets has shown that our method yields solutions of higher quality than the competing methods according to classic clustering-validation criteria, without neglecting fairness aspects.