Scaling the Queue: Reinforcement Learning for Equitable Call Classification Capacity in NYC Municipal Complaint Systems
Using AI to route 311 complaints fairly across New York City neighborhoods
New York City's 311 complaint system can't keep up with incoming calls, causing longer waits and worse service in poorer neighborhoods. Researchers built an AI system that routes complaints more intelligently—by learning that neighborhoods with repeated complaints actually need faster action, not just those with the most calls. The system reduced unfair service gaps while handling more complaints without replacing human staff.
NYC residents in low-income and communities of color have historically waited longer for building inspections and housing repairs. This AI system could cut those wait times by routing complaints to the right teams faster, meaning families get heat in winter or safe scaffolding fixed sooner. The approach also shows that fair service doesn't mean treating everyone identically—it means understanding which neighborhoods have persistent problems that need priority attention.