License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICDT.2021.13
URN: urn:nbn:de:0030-drops-137216
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/13721/
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Moumoulidou, Zafeiria ; McGregor, Andrew ; Meliou, Alexandra

Diverse Data Selection under Fairness Constraints

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LIPIcs-ICDT-2021-13.pdf (7 MB)

Abstract

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 ? 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 k_i 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.

BibTeX - Entry

@InProceedings{moumoulidou_et_al:LIPIcs.ICDT.2021.13,
  author =	{Moumoulidou, Zafeiria and McGregor, Andrew and Meliou, Alexandra},
  title =	{{Diverse Data Selection under Fairness Constraints}},
  booktitle =	{24th International Conference on Database Theory (ICDT 2021)},
  pages =	{13:1--13:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-179-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{186},
  editor =	{Yi, Ke and Wei, Zhewei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/13721},
  URN =		{urn:nbn:de:0030-drops-137216},
  doi =		{10.4230/LIPIcs.ICDT.2021.13},
  annote =	{Keywords: data selection, diversity maximization, fairness constraints, approximation algorithms}
}

Keywords: data selection, diversity maximization, fairness constraints, approximation algorithms
Collection: 24th International Conference on Database Theory (ICDT 2021)
Issue Date: 2021
Date of publication: 11.03.2021

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