The Datafication of Care in Public Homelessness Services

TL;DR

This paper explores how Toronto's homelessness system uses data and AI, revealing the balance between standardized processes and frontline heuristics amid uncertainties, and proposing holistic assessment approaches.

Contribution

It provides an ethnographic analysis of data practices in homelessness services, highlighting the limitations of predictive models and advocating for holistic decision-making tools.

Findings

Frontline workers use heuristics alongside data systems.

Client data is temporally constrained, affecting AI model validity.

Holistic assessment approaches contrast with standard risk tools.

Abstract

Homelessness systems in North America adopt coordinated data-driven approaches to efficiently match support services to clients based on their assessed needs and available resources. AI tools are increasingly being implemented to allocate resources, reduce costs and predict risks in this space. In this study, we conducted an ethnographic case study on the City of Toronto's homelessness system's data practices across different critical points. We show how the City's data practices offer standardized processes for client care but frontline workers also engage in heuristic decision-making in their work to navigate uncertainties, client resistance to sharing information, and resource constraints. From these findings, we show the temporality of client data which constrain the validity of predictive AI models. Additionally, we highlight how the City adopts an iterative and holistic client assessment approach which contrasts to commonly used risk assessment tools in homelessness, providing future directions to design holistic decision-making tools for homelessness.