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.

翻译：北美无家可归者服务体系采用协调一致的数据驱动方法，根据评估的需求和可用资源，高效地将支持服务匹配给服务对象。在此领域，人工智能工具正日益被用于分配资源、降低成本以及预测风险。在本研究中，我们对多伦多市无家可归者服务体系在不同关键节点的数据实践进行了民族志案例研究。我们展示了该市的数据实践如何为服务对象照护提供标准化流程，但一线工作者在工作中也会运用启发式决策来应对不确定性、服务对象对信息共享的抵触以及资源限制。基于这些发现，我们揭示了服务对象数据的时间性如何制约了预测性AI模型的有效性。此外，我们强调了该市采用的迭代式、整体性服务对象评估方法，这与无家可归者领域常用的风险评估工具形成对比，为未来设计面向无家可归问题的整体性决策工具提供了方向。