Who Pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies
This work addresses the deployment of AI-based solutions in social service provision, such as preventing tenant eviction, by highlighting the importance of spatial risk concentration and deployment costs, though it is incremental in refining existing models.
The paper tackles the problem of reconciling conflicting results on AI-driven scarce resource allocation by developing a framework to assess how spatial inequality affects the effectiveness of targeting policies, showing that individually targeted policies achieve considerable gains in canvassing high-risk households, even in highly segregated areas.
AI-powered scarce resource allocation policies rely on predictions to target either specific individuals (e.g., high-risk) or settings (e.g., neighborhoods). Recent research on individual-level targeting demonstrates conflicting results; some models show that targeting is not useful when inequality is high, while other work demonstrates potential benefits. To study and reconcile this apparent discrepancy, we develop a stylized framework based on the Mallows model to understand how the spatial distribution of inequality affects the effectiveness of door-to-door outreach policies. We introduce the RENT (Relative Efficiency of Non-Targeting) metric, which we use to assess the effectiveness of targeting approaches compared with neighborhood-based approaches in preventing tenant eviction when high-risk households are more versus less spatially concentrated. We then calibrate the model parameters to eviction court records collected in a medium-sized city in the USA. Results demonstrate considerable gains in the number of high-risk households canvassed through individually targeted policies, even in a highly segregated metro area with concentrated risks of eviction. We conclude that apparent discrepancies in the prior literature can be reconciled by considering 1) the source of deployment costs and 2) the observed versus modeled concentrations of risk. Our results inform the deployment of AI-based solutions in social service provision that account for particular applications and geographies.