Exploring Tradeoffs in Automated School Redistricting: Computational and Ethical Perspectives

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United States

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LLM/Chat, Ethics / responsible AI

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

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• The US public school system is administered by local school districts.

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• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
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• Each district comprises a set of schools mapped to attendance zones which are annually assessed to meet en- rollment objectives.

• Each district comprises a set of schools mapped to attendance zones which are annually assessed to meet en- rollment objectives.

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 en- rollment objectives.

• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

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• Ethics and responsible use
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• LLM/Chat
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• Completed

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Research / curriculum design context

LLM/Chat, Ethics / responsible AI

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High

High

Exploring Tradeoffs in Automated School Redistricting: Computational and Ethical Perspectives

• Fanglan Chen
• Subhodip Biswas
• Zhiqian Chen
• Shuo Lei
• Naren Ramakrishnan
• Chang-Tien Lu

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26889

https://ojs.aaai.org/index.php/AAAI/article/view/26889

https://ojs.aaai.org/index.php/AAAI/article/view/26889/26661

080_Exploring Tradeoffs in Automated School Redistricting_ Computational and Ethical Perspectives.pdf

9

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 en- rollment objectives. To support school officials in redrawing school boundaries, existing approaches have proven promis- ing but still suffer from several challenges, including 1) in- ability to scale to large school districts, 2) high computa- tional cost of obtaining compact school attendance zones, and 3) lack of ethical considerations underlying the redrawing of school boundaries. Motivated by these challenges, this paper approaches the school redistricting problem from both com- putational and ethical standpoints. First, we introduce a prac- tical framework based on Markov Chain Monte Carlo meth- ods to solve school redistricting as a graph partitioning prob- lem. Next, the advantages of adopting a modified objective function for optimizing discrete geometry to obtain compact boundaries are examined. Lastly, alternative metrics to ad- dress ethical considerations in real-world scenarios are for- mally defined and thoroughly discussed. Our findings high- light the inclusiveness and efficiency advantages of the de- signed framework and depict how tradeoffs need to be made to obtain qualitatively different school redistricting plans.

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Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

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