Contextual Stochastic Optimization for School Desegregation Policymaking - Guan, Hongzhao; Gillani, Nabeel; Simko, Tyler; Mangat, Jasmine; Van Hentenryck, Pascal (Proceedings of the AAAI Conference on Artificial Intelligence, 2025)
Most US school districts draw geographic "attendance zones" toassign children to schools based on their home address, a process thatcan replicate existing neighborhood racial/ethnic and socioeconomicstatus (SES) segregation in schools. Redrawing boundaries can reducesegregation, but estimating expected rezoning impacts is often challenging becausefamilies can opt-out of their assigned schools. This paper seeks to alleviate this societal problem by developing a jointredistricting and choice modeling framework, called redistrictingwith choices (RWC). The RWC framework is applied to a large USpublic school district to estimate how redrawing elementary schoolboundaries might realistically impact levels ofsocioeconomic segregation. The main methodological contribution ofRWC is a contextual stochastic optimization model that aims to minimizedistrict-wide segregation by integrating rezoning constraintswith a machine learning-based school choice model. The study finds that RWCyields boundary changes that might reduce segregation by a substantialamount (23%) -- but doing so might require the re-assignment of alarge number of students, likely to mitigate re-segregation thatchoice patterns could exacerbate. The results also reveal thatpredicting school choice is a challenging machine learning problem.Overall, this study offers a novel practical framework that bothacademics and policymakers might use to foster more diverse andintegrated schools.