On Machine Learning for Prediction and Prioritization in the Allocation of Scarce Societal Resources

I will talk about how the theories of local justice and of street-level bureaucracy can inform the potential application of prediction methods from machine learning in the allocation of scarce societal resources. In particular, I will discuss how separating the measurement aspects of ML methods from the allocation strategy can be helpful in ensuring that ML helps achieve societal goals. I will give some context on how these goals can be different for different domains through vignettes from our own research in allocation of services to households experiencing homelessness, K-12 educational supports, and caseworker time for providing support to those at high risk for eviction.