Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as gender or race, it becomes important to enforce fairness to avoid bias and discrimination. This has spurred significant interest in developing fair machine learning algorithms. Recently, such algorithms have been developed for monotone submodular maximization under a cardinality constraint. In this paper, we study the natural generalization of this problem to a matroid constraint. We give streaming algorithms as well as impossibility results that provide trade-offs between efficiency, quality and fairness. We validate our findings empirically on a range of well-known real-world applications: exemplar-based clustering, movie recommendation, and maximum coverage in social networks.

论文研究流式子模最大化算法中公平性的应用，提出了针对劣模最大化算法的公平机器学习算法，包括在鸽子数量受限的情况下的流式算法以及无法实现的结果。最后，通过社会网络中的最大覆盖，电影推荐和样本聚类等应用的实验验证了其有效性。

在Matroid约束下流式子模最大化中的公平性