Consensus-and-clustering opinion mapping
A real-time opinion map that plots participants into clusters and surfaces consensus statements (broad agreement across clusters) separately from divisive ones.
When AI agents can generate large volumes of distinct-looking submissions, raw counts and free-text piles stop telling an agency where genuine agreement lies. It needs to see the structure of opinion across a consultation: which positions are broadly shared, and where real disagreement sits, without a loud or heavily mobilized faction reading as the public.
The challenge is to surface that breadth from high-volume input in a way the agency can act on and defend.
Government needs confidence that what it reads as agreement reflects genuine breadth across participants, not the volume a well-organized or agent-assisted faction can produce. That means weighting positions by how widely they are shared across distinct groups, and being able to show that distinction when it explains a decision.
Structured deliberation can shut out people who need to express themselves in their own words, or who lack the language or digital access the format assumes. Keep the path open by offering a free-text route alongside the structured one, and by meeting language and accessibility needs (as the Canadian bilingual pilot did) so the method widens participation rather than narrowing it.
A clustered opinion map that ranks statements by how widely they are shared across distinct groups, so a position that bridges divides sits above one that accumulates votes within a single faction, however loud that faction is.
No surface has been built yet; the approach above is the brief for one.
Emerging (proven in multiple jurisdictions but not yet mainstream government practice)
Pol.is Platform. An open-source platform for large-scale deliberation that uses real-time clustering to reveal opinion groups. Participants write short statements and vote agree/disagree/pass on others' statements. Participants cannot reply to each other, which eliminates trolling incentives and thread derailment. The platform groups participants by voting-pattern similarity using dimensionality reduction (PCA) followed by K-means clustering, with the number of opinion groups chosen by silhouette analysis. The resulting visualization shows opinion clusters as spatial groups, consensus statements (high approval across all clusters), divisive statements, and the relative size of each group. This directly addresses the volume-vs-breadth problem: the system elevates statements that bridge divides (breadth) rather than statements that simply accumulate votes within one cluster (volume).
vTaiwan (Taiwan, 2014–present). The most prominent Pol.is deployment, used by Taiwan's government for multi-stakeholder deliberation on Uber regulation, online alcohol sales, and telemedicine. vTaiwan combines online Pol.is deliberation with face-to-face stakeholder meetings, using the clustering output to structure in-person discussion around identified areas of consensus and disagreement.
Canadian Government Pol.is Pilot (Canada, 2018). The Government of Canada deployed Pol.is six times in 2018, adapting it for bilingual (English/French) use and compliance with federal data privacy, security, and accessibility requirements. Deployments engaged 25 stakeholder groups, including a national engagement on digital disruption's impact on visual artists.
Additional Deployments. Pol.is has been used by governments in the United States, Singapore, Philippines, Finland, and Spain, as well as by civil society organizations globally.
High. Pol.is is open-source and has been successfully adapted to multiple jurisdictional contexts, including bilingual deployments; its clustering approach is language-agnostic in principle. The main barriers are cultural (governments accustomed to free-text submissions may resist structured deliberation) and institutional (Pol.is works best when its outputs feed into a defined decision-making process, as in vTaiwan).
Pol.is is a deliberation surface, not a decision engine, but by structuring opinion around shared consensus it counters the volume-as-mandate reasoning that lets a single loud signal stand in for genuine public support.