Data_Sheet_1_Reason against the machine? Future directions for mass online deliberation.docx

Designers of online deliberative platforms aim to counter the degrading quality of online debates. Support technologies such as machine learning and natural language processing open avenues for widening the circle of people involved in deliberation, moving from small groups to “crowd” scale. Numerous design features of large-scale online discussion systems allow larger numbers of people to discuss shared problems, enhance critical thinking, and formulate solutions. We review the transdisciplinary literature on the design of digital mass deliberation platforms and examine the commonly featured design aspects (e.g., argumentation support, automated facilitation, and gamification) that attempt to facilitate scaling up. We find that the literature is largely focused on developing technical fixes for scaling up deliberation, but may neglect the more nuanced requirements of high quality deliberation. Furthermore, current design research is carried out with a small, atypical segment of the world's population, and little research deals with how to facilitate and accommodate different genders or cultures in deliberation, counter pre-existing social inequalities, build motivation and self-efficacy in certain groups, or deal with differences in cognitive abilities and cultural or linguistic differences. We make design and process recommendations to correct this course and suggest avenues for future research.