TRACK 2: Collaborative Intelligence: Humans, Crowds, and Machines

Track Chairs: Helen K. Liu (National Taiwan University, Taiwan), Lisa Schmidthuber (WU Vienna University of Economics and Business, Austria), Seok-Jin Eom (Seoul National University, South Korea)

The collaborative intelligence track aims to investigate how human, crowd, and machine can complement each other to enhance public services and policies, such as healthcare services, citizen-government communication, bias and discretion reduction, smart city planning, etc. However, while the adoption of AI may enhance the citizens’ participation experience, there are potential ethical issues and implementation challenges in designing an optimal collaborative intelligence that includes both human collective intelligence and artificial intelligence. The collaborative intelligence track invites researchers and practitioners to accumulate scholarly papers that explore the interactions of human, crowd, and/or machine. Possible topics include strategies for collaborative intelligence or platforms in the public sector, designs for machine and human interaction in public services or policy making, comparisons of outputs and bias from AI, experts, and/or collective intelligence, values in collaborative intelligence management and governance, best practices of collaborative intelligence in the public sector, ethical concerns or guidelines for applying collective intelligence, or other similar topics and relevant approaches.