2020/10/18 by Zafeiria Moumoulidou, Andrew McGregor, Moumoulidou, Zafeiria +3 · 2 citations
Computer Science · Social Sciences · #Data Structures and Algorithms (cs.DS) #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Mobile Crowdsensing and Crowdsourcing #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2010.09141
openalex publication_date 2020/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Diversity is an important principle in data selection and summarization, facility location, and recommendation systems. Our work focuses on maximizing diversity in data selection, while offering fairness guarantees. In particular, we offer the first study that augments the Max-Min diversification objective with fairness constraints. More specifically, given a universe U of n elements that can be partitioned into m disjoint groups, we aim to retrieve a k-sized subset that maximizes the pairwise minimum distance within the set (diversity) and contains a pre-specified ki number of elements from each group i (fairness). We show that this problem is NP-complete even in metric spaces, and we propose three novel algorithms, linear in n, that provide strong theoretical approximation guarantees for different values of m and k. Finally, we extend our algorithms and analysis to the case where groups can be overlapping.