Title: Fair Bayesian Model-Based Clustering Show Chinese title

Title: 公平的贝叶斯基于模型的聚类

Abstract: Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that the proportions of each sensitive group are similar in all clusters. Most existing group-fair clustering methods are based on the $K$-means clustering and thus require the distance between instances and the number of clusters to be given in advance. To resolve this limitation, we propose a fair Bayesian model-based clustering called Fair Bayesian Clustering (FBC). We develop a specially designed prior which puts its mass only on fair clusters, and implement an efficient MCMC algorithm. Advantages of FBC are that it can infer the number of clusters and can be applied to any data type as long as the likelihood is defined (e.g., categorical data). Experiments on real-world datasets show that FBC (i) reasonably infers the number of clusters, (ii) achieves a competitive utility-fairness trade-off compared to existing fair clustering methods, and (iii) performs well on categorical data. Show Chinese abstract

Abstract: 公平聚类随着机器学习技术的发展以及对可信人工智能需求的增长，已成为一项具有社会意义的任务。群体公平性确保每个敏感组在所有聚类中的比例相似。 大多数现有的群体公平聚类方法基于$K$- 均值聚类，因此需要预先给出实例之间的距离和聚类的数量。 为了解决这一限制，我们提出了一种基于公平贝叶斯模型的聚类方法，称为公平贝叶斯聚类（FBC）。 我们开发了一种专门设计的先验，该先验仅集中在公平聚类上，并实现了一个高效的MCMC算法。 FBC 的优势在于它可以推断聚类的数量，并且只要定义了似然函数就可以应用于任何数据类型（例如，分类数据）。 对真实数据集的实验表明，FBC（i）合理地推断出聚类数量，（ii）与现有的公平聚类方法相比实现了竞争性的效用-公平性权衡，（iii）在分类数据上表现良好。