The Datafication of Care in Public Homelessness Services

要旨

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.

受賞
Honorable Mention
著者
Erina Seh-Young Moon
University of Toronto, Toronto, Ontario, Canada
Devansh Saxena
University of Wisconsin-Madison, Madison, Wisconsin, United States
Dipto Das
University of Toronto, Toronto, Ontario, Canada
Shion Guha
University of Toronto, Toronto, Ontario, Canada
DOI

10.1145/3706598.3713232

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713232

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Personal Data and Decision-Making

Annex Hall F204
6 件の発表
2025-04-28 20:10:00
2025-04-28 21:40:00
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