Exploring Tradeoffs in Automated School Redistricting: Computational and Ethical Perspectives

Authors

  • Fanglan Chen Virginia Tech
  • Subhodip Biswas Virginia Tech
  • Zhiqian Chen Mississippi State University
  • Shuo Lei Virginia Tech
  • Naren Ramakrishnan Virginia Tech
  • Chang-Tien Lu Virginia Tech

DOI:

https://doi.org/10.1609/aaai.v37i13.26889

Keywords:

School Redistricting, Spatial Optimization, Graph Partition, Sampling

Abstract

The US public school system is administered by local school districts. Each district comprises a set of schools mapped to attendance zones which are annually assessed to meet enrollment objectives. To support school officials in redrawing attendance boundaries, existing approaches have proven promising but still suffer from several challenges, including: 1) inability to scale to large school districts, 2) high computational cost of obtaining compact school attendance zones, and 3) lack of discussion on quantifying ethical considerations underlying the redrawing of school boundaries. Motivated by these challenges, this paper approaches the school redistricting problem from both computational and ethical standpoints. First, we introduce a practical framework based on sampling methods to solve school redistricting as a graph partitioning problem. Next, the advantages of adopting a modified objective function for optimizing discrete geometry to obtain compact boundaries are examined. Lastly, alternative metrics to address ethical considerations in real-world scenarios are formally defined and thoroughly discussed. Our findings highlight the inclusiveness and efficiency advantages of the designed framework and depict how tradeoffs need to be made to obtain qualitatively different school redistricting plans.

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Published

2023-09-06

How to Cite

Chen, F., Biswas, S., Chen, Z., Lei, S., Ramakrishnan, N., & Lu, C.-T. (2023). Exploring Tradeoffs in Automated School Redistricting: Computational and Ethical Perspectives. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15912-15920. https://doi.org/10.1609/aaai.v37i13.26889