The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes, highlights its transformative potential while underscoring the pressing need for thorough risk assessments. Despite its growing presence, evaluations of risks associated with AI-driven systems in the public sector remain insufficiently explored. Building upon an established taxonomy of AI risks derived from diverse government policies and corporate guidelines, we investigate the critical risks posed by generative AI in the public sector while extending the scope to account for its multimodal capabilities. In addition, we propose a Systematic dAta generatIon Framework for evaluating the risks of generative AI (SAIF). SAIF involves four key stages: breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types. It ensures the systematic and consistent generation of prompt data, facilitating a comprehensive evaluation while providing a solid foundation for mitigating the risks. Furthermore, SAIF is designed to accommodate emerging jailbreak methods and evolving prompt types, thereby enabling effective responses to unforeseen risk scenarios. We believe that this study can play a crucial role in fostering the safe and responsible integration of generative AI into the public sector.

翻译：生成式人工智能在公共部门的迅速应用，涵盖从自动化公共援助到福利服务及移民流程的多样化场景，既彰显了其变革潜力，也凸显了进行彻底风险评估的迫切需求。尽管其应用日益广泛，针对公共部门人工智能驱动系统相关风险的评估仍缺乏充分探索。基于从各类政府政策与企业指南中提炼出的人工智能风险分类体系，本研究深入探讨了生成式人工智能在公共部门可能引发的关键风险，并将研究范畴扩展至其多模态能力带来的影响。此外，我们提出了一个用于评估生成式人工智能风险的系统化数据生成框架（SAIF）。该框架包含四个核心阶段：风险解构、场景设计、越狱方法应用及提示类型探索。SAIF通过确保提示数据的系统化与一致性生成，既支撑全面风险评估，也为风险缓解策略提供了坚实基础。同时，该框架具备良好扩展性，能够兼容新兴越狱方法与动态演进的提示类型，从而有效应对未预见的风险情境。我们相信，本研究对推动生成式人工智能在公共部门实现安全、负责任的应用具有重要价值。