License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2020.5
URN: urn:nbn:de:0030-drops-120215
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Jung, Christopher ; Kannan, Sampath ; Lutz, Neil

Service in Your Neighborhood: Fairness in Center Location

LIPIcs-FORC-2020-5.pdf (5 MB)


When selecting locations for a set of centers, standard clustering algorithms may place unfair burden on some individuals and neighborhoods. We formulate a fairness concept that takes local population densities into account. In particular, given k centers to locate and a population of size n, we define the "neighborhood radius" of an individual i as the minimum radius of a ball centered at i that contains at least n/k individuals. Our objective is to ensure that each individual has a center that is within at most a small constant factor of her neighborhood radius.
We present several theoretical results: We show that optimizing this factor is NP-hard; we give an approximation algorithm that guarantees a factor of at most 2 in all metric spaces; and we prove matching lower bounds in some metric spaces. We apply a variant of this algorithm to real-world address data, showing that it is quite different from standard clustering algorithms and outperforms them on our objective function and balances the load between centers more evenly.

BibTeX - Entry

  author =	{Christopher Jung and Sampath Kannan and Neil Lutz},
  title =	{{Service in Your Neighborhood: Fairness in Center Location}},
  booktitle =	{1st Symposium on Foundations of Responsible Computing (FORC 2020)},
  pages =	{5:1--5:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-142-9},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{156},
  editor =	{Aaron Roth},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-120215},
  doi =		{10.4230/LIPIcs.FORC.2020.5},
  annote =	{Keywords: Fairness, Clustering, Facility Location}

Keywords: Fairness, Clustering, Facility Location
Collection: 1st Symposium on Foundations of Responsible Computing (FORC 2020)
Issue Date: 2020
Date of publication: 18.05.2020

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