“Homelessness presents a long-standing problem worldwide. Like other welfare services, homeless services have gained increased traction in Machine Learning (ML) research. Unhoused persons are vulnerable and using their data in the ML pipeline raises serious concerns about the unintended harms and consequences of prioritizing different ML values. … Unhoused persons were lost (i.e., humans were deprioritized) at multi-level ML abstraction of predictors, categories and algorithms. Our findings illuminate potential pathways forward … by situating humans at the center to support this vulnerable community.”

Find the paper and full list of authors in the Conference on Human Factors in Computing Systems, 2023 proceedings.