论文标题
公平的聚类？
Whither Fair Clustering?
论文作者
论文摘要
在相对繁忙的机器学习的领域内，由分类公平研究主导，聚类的公平性开始引起人们最近的关注。在该职位论文中，我们评估了现有的公平聚类工作，并观察到有几个方向尚待探索，并假定在Outlook中，公平聚类的最先进是非常狭och的。我们认为，扩大针对目标的规范原则，表征无法完全实现目标的短缺，并且利用下游过程的知识可以显着扩大公平聚类研究中的研究范围。在越来越多地使用聚类和无监督的学习来做出对人类生命至关重要的决策的时候，我们认为扩大公平聚类的范围具有巨大的意义。
Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention. In this position paper, we assess the existing work in fair clustering and observe that there are several directions that are yet to be explored, and postulate that the state-of-the-art in fair clustering has been quite parochial in outlook. We posit that widening the normative principles to target for, characterizing shortfalls where the target cannot be achieved fully, and making use of knowledge of downstream processes can significantly widen the scope of research in fair clustering research. At a time when clustering and unsupervised learning are being increasingly used to make and influence decisions that matter significantly to human lives, we believe that widening the ambit of fair clustering is of immense significance.
