Abstract
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.
- Existing literature prioritizes technical fixes for scaling up deliberation—such as handling volume and structure—over the nuanced requirements of high-quality deliberation:
- Homogenous test groups: Current design relies on WEIRD
- Limitations of automated tools: automated tools manage data volume, but lack the discretion and nuanced intervention capabilities of human facilitators, such as mirroring perspectives or sensitively enforcing norms. These tools often impose expert-driven ideals regarding what constitutes a “valid” argument, excluding those who rely on storytelling or personal experience.
- Exclusionary structures: technical designs overlook how pre-existing social inequalities, like difference in cognitive abilities, literacy, ore self-efficacy, affect participation.
- Process recommendations
- Utilize “Solution Generation” for Synthesis: Rather than forcing users to read all comments, use AI to synthesize scattered discussions into a cohesive list of proposed success metrics or answers, reducing the cognitive load of decision-making.
- Design for “Perspective-Taking”: Address the “unawareness of others’ backgrounds” by explicitly prompting users to consider the viewpoints of diverse stakeholders (e.g., marginalized groups affected by the public service) before they finalize their voting or suggestions.
- Prioritize “Argumentative Quality” Metrics: When evaluating your tool’s success, move beyond engagement counts (quantity) and measure “deliberativeness,” “persuasiveness,” and “constructiveness” to ensure the tool is actually producing useful policy decisions.