Group decision-making support has been a longstanding research topic. Achieving productive consensus in organizations remains challenging, as it requires participants to engage in discussions with broad perspectives and flexible thinking, and to understand problems from multiple perspectives. We have developed technologies and systems that support awareness of diverse values and viewpoints, aiming to enhance the productivity of discussions. This paper presents a discussion agent based on large language models (LLMs) that intervenes in human discussions to stimulate participants by introducing new perspectives, ideas, or critiques, thereby promoting more productive discussion. The agent is designed to change its behavior according to assigned roles within the discussion. We experimentally examined the impact of the agent’s behavior on human discussions.

This study analyzes the value conflict of a paper on fan fiction writing that used online fan fiction novels as a source to extract and filter sexual expressions from text. The boundaries of public and private information are ambiguous because users are not always aware of or have agreed to the fact that their content is to be used openly. The case was complicated by the fact that the use of these data by researchers violated an unconsciously infringed upon right of a vulnerable community with a weak legal position. This paper describes the debate on this topic among researchers from engineering and humanities fields on whether the purpose of the research was ethically acceptable; how the systems can be embedded in ethical values; and what ethical, legal, social, and educational lessons are appropriate for governance of artificial intelligence (AI). Our analysis aimed not only to clarify the abstract concept of privacy but also to make changes to the submission guidelines for authors. We hope that our analysis contributes to the governance of ethical AIs and AI ethics on handling sensitive aspects of online activities.

Due to the rise of artificial intelligence (AI) technology, discussions are progressing on how robots could replace human labor. Conventional surveys have suggested that human labor is expected to gradually be replaced as tasks become automated. We conducted a survey at the world's first robot hotel recently opened in Japan - called a Henn-na hotel (“strange/change hotel”) in Japanese - which already uses robots for most of the work. We discovered that human labor is divided into small tasks, and that robot actions affect human emotional control. However, the hotel not only divides human work but also reconstructs it from tasks. Moreover, the purpose of reconstruction is not simply for replacement of works. Such modification of task is often observed taking place in humansystem interactions. It is an extremely creative process of labor emerging in this area.

Multiagent-based simulation (MABS) is a very active interdisciplinary area bridging multiagent research and social science. The key technology to conduct truly useful MABS is agent modeling for reproducing realistic behaviors. In order to make agent models realistic, it seems natural to learn from human behavior in the real world. The challenge presented in this paper is to obtain an individual behavior model by using participatory modeling in the traffic domain. We show a methodology that can elicit prior knowledge for explaining human driving behavior in specific environments, and then construct a driving behavior model based on the set of prior knowledge. In the real world, human drivers often perform unintentional actions, and occasionally, they have no logical reason for their actions. In these cases, we cannot rely on prior knowledge to explain them. We are forced to construct a behavior model with an insufficient amount of knowledge to reproduce the driving behavior. To construct such individual driving behavior model, we take the approach of using knowledge from others to complement the lack of knowledge from the target. To clarify that the behavior model including prior knowledge from others offers individuality in driving behavior, we experimentally confirm that the driving behaviors reproduced by the hybrid model correlate reasonably well with human behavior.