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All Politics is Local: Redistricting via Local Fairness

Publication ,  Conference
Ko, SH; Taylor, E; Agarwal, PK; Munagala, K
Published in: Advances in Neural Information Processing Systems
January 1, 2022

In this paper, we propose to use the concept of local fairness for auditing and ranking redistricting plans. Given a redistricting plan, a deviating group is a population-balanced contiguous region in which a majority of individuals are of the same interest and in the minority of their respective districts; such a set of individuals have a justified complaint with how the redistricting plan was drawn. A redistricting plan with no deviating groups is called locally fair. We show that the problem of auditing a given plan for local fairness is NP-complete. We present an MCMC approach for auditing as well as ranking redistricting plans. We also present a dynamic programming based algorithm for the auditing problem that we use to demonstrate the efficacy of our MCMC approach. Using these tools, we test local fairness on real-world election data, showing that it is indeed possible to find plans that are almost or exactly locally fair. Further, we show that such plans can be generated while sacrificing very little in terms of compactness and existing fairness measures such as competitiveness of the districts or seat shares of the plans.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

ISBN

9781713871088

Publication Date

January 1, 2022

Volume

35

Related Subject Headings

  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

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ICMJE
MLA
NLM
Ko, S. H., Taylor, E., Agarwal, P. K., & Munagala, K. (2022). All Politics is Local: Redistricting via Local Fairness. In Advances in Neural Information Processing Systems (Vol. 35).
Ko, S. H., E. Taylor, P. K. Agarwal, and K. Munagala. “All Politics is Local: Redistricting via Local Fairness.” In Advances in Neural Information Processing Systems, Vol. 35, 2022.
Ko SH, Taylor E, Agarwal PK, Munagala K. All Politics is Local: Redistricting via Local Fairness. In: Advances in Neural Information Processing Systems. 2022.
Ko, S. H., et al. “All Politics is Local: Redistricting via Local Fairness.” Advances in Neural Information Processing Systems, vol. 35, 2022.
Ko SH, Taylor E, Agarwal PK, Munagala K. All Politics is Local: Redistricting via Local Fairness. Advances in Neural Information Processing Systems. 2022.

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

ISBN

9781713871088

Publication Date

January 1, 2022

Volume

35

Related Subject Headings

  • 1702 Cognitive Sciences
  • 1701 Psychology