Governments are the primary providers of essential public services and are responsible for delivering them effectively. In high-stakes decision-making domains such as child welfare (CW), agencies must protect children without unnecessarily prolonging a family's engagement with the system. With growing optimism around AI, governments are pushing for its integration but concerns regarding feasibility and harms remain. Through collaborations with a large Canadian CW agency, we examined how LocalLLM and BERTopic models can track CW case progress. We demonstrate how the tools can potentially assist workers in opportunistically addressing gaps in their work by signaling case progress/deviations. And yet, we also show how they fail to detect case trajectories that require discretionary judgments grounded in social work training, areas where practitioners would actually want support to pre-emptively address substantive case concerns. We also provide a roadmap of future participatory directions to co-design language tools for/with the public sector.

翻译：政府是基本公共服务的主要提供者，并负有高效供给的责任。在儿童福利等高风险决策领域，相关机构必须在保护儿童权益的同时，避免让家庭不必要地长期滞留于福利体系之中。随着人工智能技术日益受到重视，各国政府正积极推进其应用，但对技术可行性及潜在危害的担忧依然存在。通过与加拿大大型儿童福利机构的合作，本研究探讨了如何利用LocalLLM与BERTopic模型追踪个案进展。我们展示了这些工具如何通过提示案件进展/偏差，辅助工作人员及时弥补工作中的疏漏。然而，研究也发现这些工具难以识别需要基于社会工作专业训练进行自由裁量的案件发展轨迹——而这正是实务工作者真正需要获得支持以预先应对实质性案件关切的领域。最后，本文为公共部门语言工具的协同设计提出了未来参与式发展路径。