Opinion mapping by consensus and clustering
Mapping where genuine agreement sits across a consultation, by weighting positions on how widely they are shared across distinct groups rather than how often one group repeats them. A position's reported weight reflects how many different groups hold it, which volume alone can't counterfeit.
The impact of agents
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.
What must be verified
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.
Protecting access
Structured deliberation can exclude users with limited language or literacy skills, and those with limited connectivity. It also excludes along a subtler line: someone who can only say what they mean in their own words is deterred by a format built around voting on other people's statements. Their view never reaches the map. It gets recorded as silence.
Keeping the path open
- Run a free-text route alongside the structured one, feeding what it gathers into the same analysis rather than a side pile.
- Meet language and accessibility needs up front, as the Canadian bilingual deployments did, so the method widens participation rather than narrowing it to the format-fluent.
Response surface
Statements are ranked by how widely they are shared across groups, so a position that bridges divides outranks one a single faction repeats.
This statement has support across all three clusters.
The map structures deliberation among clusters rather than ranking statements for a decision. Its job is to answer “how widely is this shared?” — a question raw vote counts cannot answer.
Maturity
Emerging (proven in multiple jurisdictions but not yet mainstream government practice)
Precedents
Pol.is, Small et al. The open-source platform runs large-scale deliberation by real-time clustering: participants write short statements and vote agree, disagree, or pass on others'. It has no reply mechanism at all, a design its authors credit with reducing the ability to take a conversation off topic and with disincentivizing trolling, and it groups participants by voting-pattern similarity using dimensionality reduction followed by K-means clustering. The visualization elevates statements that bridge divides over statements that accumulate votes inside one cluster.
vTaiwan, Taiwan. Taiwan's government used the platform for deliberation among affected groups on Uber regulation, online alcohol sales, and telemedicine. It combines online deliberation with face-to-face meetings among those groups, using the clustering output to structure in-person discussion around identified areas of consensus and disagreement. The clustering feeds a process, and does not conclude one.
The Canadian government pilot. The Government of Canada deployed the platform six times in one year, adapting it for bilingual use and for compliance with federal privacy, security, and accessibility requirements. Deployments engaged 25 groups of participants, including a national engagement on digital disruption's impact on visual artists. Adapting it to a federal government's own obligations was the work the pilot did.
What carries over to agent use
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 decisions process, as in vTaiwan).
Where things go wrong
Pol.is is a deliberation surface, not a decision engine. The failure mode is treating its clustering output as the decision itself rather than as input to one a person still owns, which drops the accountability the mapping was meant to preserve. The clustering itself can be gamed: a coordinated or agent-run bloc voting in a matched pattern across many accounts can register as its own opinion cluster, and a statement that bloc endorses then reads as bridging real divides rather than as one faction's position. Verifying that each voting account traces to a distinct participant is what keeps a manufactured cluster from counting as a genuine one. That guard needs an identified channel, where identity is established before a vote is accepted. A consultation seeking views often runs open instead, and identity can't be required there as a condition of being heard. The round should say which condition it runs under. Where it runs open, the work falls to the agency rather than the participant: report the map as the structure of the positions put rather than a count of the people holding them, and test a cluster against other evidence before a decision cites it.
Sources
5 references
The instrument, the operating deployment, or the official record itself.
Writing about the subject rather than the framework itself, including vendor commentary.