The Promises and Perils of using LLMs for Effective Public Services
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arXiv
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| Format: | Preprint |
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2026
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| author | Moon, Erina Seh-Young Tamura, Matthew Zhai, Angelina Habib, Nuzaira Shirazi, Behnaz Kassam, Altaf Saxena, Devansh Guha, Shion |
| author_facet | Moon, Erina Seh-Young Tamura, Matthew Zhai, Angelina Habib, Nuzaira Shirazi, Behnaz Kassam, Altaf Saxena, Devansh Guha, Shion |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15163 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Promises and Perils of using LLMs for Effective Public Services Moon, Erina Seh-Young Tamura, Matthew Zhai, Angelina Habib, Nuzaira Shirazi, Behnaz Kassam, Altaf Saxena, Devansh Guha, Shion Human-Computer Interaction Computers and Society 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. |
| title | The Promises and Perils of using LLMs for Effective Public Services |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2601.15163 |