AI 中文总结
该研究定义了政府服务的智能体泛滥问题，基于84起案例数据集分析其广泛存在性，开发风险矩阵评估高风险服务，梳理政府应对措施并推荐兼顾公平的缓解方案。
AI 中文摘要
AI智能体让公众与政府互动变得更便捷，例如帮助公众申请福利、理解复杂政策、表达意见。尽管提升服务可及性有益，但由此引发的需求激增可能让准备不足的政府服务不堪重负。我们将这种激增称为政府服务的智能体泛滥（简称“泛滥”），并做出三项贡献：第一，基于收集的11个司法管辖区的84起潜在泛滥案例数据集，我们认为泛滥如今可能已广泛存在，且大多通过大型语言模型（LLMs）低成本生成文本实现；第二，我们评估哪些服务最易受泛滥影响，开发了风险矩阵分析服务的暴露程度，指出近期风险最高的是具有财务吸引力但复杂的服务；第三，我们梳理了政府应对泛滥的可能措施，先例表明这些措施可能足以阻止大多数泛滥案例，但部署最快的措施——如收费等制造摩擦的手段——往往会牺牲公共服务的公平可及性。因此，我们最后推荐了近期行动，以期让政府在不引发这种权衡的情况下缓解泛滥问题。
英文摘要
AI agents are making it easier for the public to interact with government, such as by helping them apply for benefits, understand complex policies, and make their opinions heard. Although improving service accessibility is beneficial, any resulting surges in demand could strain unprepared government services. We term such surges agentic flooding of government services ("flooding") and provide three contributions. First, based on a collected dataset of 84 potential cases of flooding across 11 jurisdictions, we posit that flooding is likely occurring widely today, mostly through large language models (LLMs) generating text cheaply. Second, we evaluate what services are most exposed to flooding. We develop a risk matrix to analyze a service's exposure, and suggest that near-term risk is highest for financially attractive, but complex services. Finally, we map possible government responses to flooding. Precedent suggests these responses will likely be sufficient to stop most cases of flooding, but the fastest to deploy - friction-inducing measures like fees - often trade off equitable access to public services. Accordingly, we close by recommending near-term actions that may allow governments to mitigate flooding without invoking this trade-off.
CommentsTo appear in the proceedings of the 9th AAAI Conference on AI, Ethics, and Society (AIES), October 12-14, 2026
