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Democratizing Big Data and Complexity Science by weaving participatory CollectiveIntelligence into FuturICT.

"MIT Center for Collective Intelligence"

If the goal is to accelerate societal learning, the interplay between scientists, policymakers and the wider public will be critical. However, the existing patterns of interaction leave much to be desired. First, the science-policy relationship is often difficult and dysfunctional. Second, the international governance infrastructure - the United Nations, World Bank, WTO and others - was designed to meet the needs of the post-WW2 era and is ill-adapted to the interconnected and transdisciplinary challenges it now faces. And finally, our main public communication channels seem better attuned to the linear and polarised narrative of crisis than to the nuanced, detailed, anticipatory work of crisis avoidance or minimisation.

We present a novel platform for supporting public deliberation on difficult decisions. ConsiderIt guides people to reflect on tradeoffs and the perspectives of others by framing interactions around pro/con points that participants create, adopt, and share. ConsiderIt surfaces the most salient pros and cons overall, while also enabling users to drill down into the key points for different groups. We deployed ConsiderIt in a contentious U.S. state election, inviting residents to deliberate on nine ballot measures. We discuss ConsiderIt's affordances and limitations, enriched with empirical data from this deployment. We show that users often engaged in normatively desirable activities, such as crafting positions that recognize both pros and cons, as well as points written by people who do not agree with them.