Research on the Application of Generative AI in Public Services and Its Reshaping of Social Policies

This study explores the application of generative artificial intelligence in the public service sector and its impact on social policies. By combining a systematic literature review and controllable scenario simulation, the technical and policy effectiveness of five typical scenarios (medical triage, welfare qualification review, driver's license renewal, municipal consultation, and emergency preparedness) were evaluated. The results show that the optimized model increased the response speed by 70% and the manual verification success rate reached 88%. The simulation calculation shows that the initial resolution rate increased by 40% and the policy development cycle was shortened by ten days. Despite the significant improvement in efficiency, occasional content distortions were observed in the model, while revealing governance challenges in terms of fairness, transparency, and accountability. Based on this, it is proposed to implement three measures: hierarchical manual supervision, continuous algorithm auditing, and dynamic regulatory sandboxes, to achieve responsible technology deployment. This study, for the first time, verified the correlation mechanism between generative AI performance and policy indicators through experimental data, providing an operational solution to equip technological innovation and institutional safeguards. Detailed simulation parameters are provided in Appendix B.