Keynote/Invited Speakers

Mary Lou Maher

University of Sydney 🇦🇺

Human-AI Ecosystem as a basis for AI Governance and Education

18th November, (Time: 9:10-10:10) (Confirmed)

Abstract:

The Human-AI Ecosystem is framed as a dynamic network composed of people, data, and AI systems that collectively drive the creation, evolution and implementation of AI applications and solutions. This framework acknowledges the relevance of human identity at each layer: Creators, Creations, and Consequences. This presentation will highlight the various ways that human identity influences the socio-economic and cultural-political factors within the AI ecosystem. These factors shape the interactions and relationships between the layers, the actors, and the impact. From this ecosystem representation, we can identify the research approaches for studying AI consequences in governance and education.

Bio:

Professor Mary Lou Maher has recently joined the School of Computer Science at the University of Sydney. Mary Lou provides leadership in CS education, innovations in CS curriculum and pedagogy, and the impact of AI on how students learn. Most recently, she was the Director of Research Community Initiatives at the Computing Research Association (CRA). In this role, she led the Computing Community Consortium in research visioning activities and in securing funding for 12 AI education research projects. She has held appointments at Carnegie Mellon University, MIT, Columbia University, the US National Science Foundation, and the University of North Carolina at Charlotte. Her AI research areas include AI Literacy and Identity, Human-Centered AI, Human-AI Co-Creativity, and CS Education. 

Shun Okuhara

Mie University 🇯🇵

Consensus Building with AI Agents: Towards an Augmented Democracy

18th November, (Time: 16:00-17:00) (Confirmed)

Abstract (TBD)

Deliberative democracy relies on rational dialogue and fair reasoning. As AI agents increasingly engage in negotiation, mediation, and explanation, new questions arise about how consensus is formed among humans and machines. This keynote introduces methods for enabling transparent and inclusive decision-making through AI agents that follow explicit negotiation rules and consensus protocols. By applying the Deliberative Quality Index (DQI) to both AI–AI and human–AI dialogues, we examine deliberative quality in terms of justification, respect, and reciprocity, based on analysis of real discussion data. The talk also highlights empirical findings from studies of online discussions, showing how AI agents can evaluate discourse, detect polarization, and promote understanding. Ultimately, it envisions a form of augmented democracy, where AI collaborates with humans to deliberate, share understanding, and form legitimate consensus through transparent, explainable interaction.

Bio

Shun Okuhara is an Associate Professor at the Graduate School of Engineering, Mie University, Japan. His research focuses on artificial intelligence agents for negotiation, consensus building, and deliberative dialogue. He leads projects on explainable and trustworthy AI, social simulation, and augmented democracy, exploring how human–AI collaboration can enhance fairness, transparency, and collective decision-making. He has contributed to international collaborations with the National Research Council Canada, Carleton University, and Kyoto University, and serves as a Board Member of ACIS International. His interdisciplinary work integrates multi-agent systems, large language models, and social computing to design AI that participates in, explains, and supports democratic dialogue.