Workshop Design for Municipal Policy Making Using Generative AI

This paper aims to enhance the efficiency of participatory workshops (WS) in municipalities by proposing a hybrid WS support framework that combines Human-in-the-Loop (HITL) and Machine-in-the-Loop (MITL) approaches utilizing generative AI. In the HITL process, generative AI is regarded as workshop participants, with a human facilitator collaborating with the AI to achieve rapid and comprehensive problem identification and organization. In contrast, the MITL process uses outputs generated by the AI as the support for discussions among human participants. This hybrid approach ensures that both human expertise and AI capabilities are optimally utilized. By strategically applying these processes across different WS phases, it becomes possible to efficiently progress through the WS with minimal information loss and achieve the desired outputs. Specifically, in the HITL process, we present a novel methodology using facilitation-based prompts, providing concrete guidance for WS designers. The proposed framework and HITL methodology were applied in actual municipalities, resulting in the successful extraction and organization of problems within a short timeframe, ultimately achieving the objectives of the overall WS process. The application showed that the framework and methodology can significantly reduce the time and resources required for effective WS execution. The findings of this study offer a new perspective on WS design and operation, supporting more efficient and effective policy making. Future challenges include expanding the application scope of the framework and methodology to other WS phases and analytical techniques, and exploring its applicability to other domains. This will enable more organizations to leverage generative AI for effective decision-making.