Enabling Large-Scale Deliberation Using Attention-Mediation Metrics

Abstract

Humanity now finds itself faced with a range of highly complex and controversial challenges – such as climate change, the spread of disease, international security, scientific collaborations, product development, and so on – that call upon us to bring together large numbers of experts and stakeholders to deliberate collectively on a global scale. Collocated meetings can however be impractically expensive, severely limit the concurrency and thus breadth of interaction, and are prone to serious dysfunctions such as polarization and hidden profiles. Social media such as email, blogs, wikis, chat rooms, and web forums provide unprecedented opportunities for interacting on a massive scale, but have yet to realize their potential for helping people deliberate effectively, typically generating poorly-organized, unsystematic and highly redundant contributions of widely varying quality. Large-scale argumentation systems represent a promising approach for addressing these challenges, by virtue of providing a simple systematic structure that radically reduces redundancy and encourages clarity. They do, however, raise an important challenge. How can we ensure that the attention of the deliberation participants is drawn, especially in large complex argument maps, to where it can best serve the goals of the deliberation? How can users, for example, find the issues they can best contribute to, assess whether some intervention is needed, or identify the results that are mature and ready to “harvest”? Can we enable, for large-scale distributed discussions, the ready understanding that participants typically have about the progress and needs of small-scale, collocated discussions?This paper will address these important questions, discussing (1) the strengths and limitations of current deliberation technologies, (2) how argumentation technology can help address these limitations, and (3) how we can use attention-mediation metrics to enhance the effectiveness of large-scale argumentation-based deliberations.