After automation: Homelessness prioritization algorithms and the future of care labor

Abstract

People experiencing homelessness seek support from homeless services systems that increasingly rely on prioritization algorithms to determine who is the most deserving of scarce resources. In this paper, we argue that algorithmic harms in homeless services require a reparative approach that takes the data work of care workers seriously. Building on Davis, Williams, and Yang's concept of algorithmic reparation, we present a qualitative study that examines the intertwining of data work and care labor of 15 care workers. We show how they wrestle with the ethics of algorithmic prioritization and develop workarounds that allow them to advocate for their clients. We contribute an empirical understanding of how care workers provide care under homeless services systems that equate data work with care labor to justify work intensification. Our findings have implications for understanding the future of care labor in datafied conditions and the social and political ramifications of algorithmically mediated care.