The Datafication of Care in Public Homelessness Services

2025-02-13

AI Summary

This study examines the tensions inherent in “care digitization” within Toronto’s homelessness services system. Drawing on ethnographic fieldwork, participant observation, in-depth interviews, and data practice tracing, it identifies a fundamental conflict between standardized data collection protocols and frontline workers’ heuristic decision-making—shaped by uncertainty, barriers to information sharing, and resource constraints. Key findings reveal that client data latency critically undermines the validity of predictive AI models, while static risk assessment tools fail to capture dynamic, person-centered care needs. The paper introduces the critical analytic framework of “care digitization,” advocating for temporally sensitive, iterative, and holistic assessments over fragmented scoring systems. It further provides empirical grounding and core design principles for developing contextually adaptive, practically viable AI-augmented decision-support tools aligned with real-world care workflows.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Algorithmic accountability and transparency on the web

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.

Problem

• AI tools for resource allocation in homelessness
• Ethnographic study on Toronto's data practices
• Challenges in predictive AI model validity

Innovation

• AI tools for resource allocation
• Ethnographic case study methodology
• Iterative holistic client assessment