Contribution to the handbook
Priority · Assessed 2026-10-28
Scale is a consequential implementation and testing limit for a method proposed for citizen deliberation. The evidence distinguishes groups tested only up to five from the theoretical possibility of hundreds, without establishing performance at that scale or suitability for governance.
Does this challenge the goal?
None identified
The question asks whether a deliberation method can scale; it does not contest the handbook’s goals, beneficiary boundary, or open choice of governance arrangements.
The question identifies an inquiry, not evidence that the method is suitable for governance at larger scale.
Why this category?
- scaling limit
- test scope
The supplied evidence reports tests only up to five people and a theoretical possibility of hundreds; it does not establish performance or fairness at larger scales.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
Tests, stop conditions and unresolved work →
Read the exploration goalDescribe the proposed basis for scaling the method and distinguish that possibility from the group sizes actually tested.
What we can say so far
The authors propose that the method could support groups of hundreds if paired with language models able to handle much longer texts. This is a theoretical possibility, not a demonstrated scaling result: the reported discussions tested groups only up to five people. A 200-person virtual assembly was also reported, but that is the assembly’s total participant count, not evidence that a single deliberating group was that large.
What remains open
- The evidence does not establish whether performance, accuracy, or fairness would hold as group size increases.
Conditions and limits
- The hundreds-of-people figure is an author-proposed possibility conditional on models handling much longer texts, not a tested capacity.
- The source is an AI interpretation of the readable article text; supplementary materials were not included.
Objections to consider
- The typical five-person setup limits what the experiment demonstrates about larger groups; the answer preserves that limit and distinguishes it from the authors’ theoretical proposal.
- The reported 200 participants could be mistaken for a 200-person deliberating group. The answer distinguishes assembly-wide participant count from group size and reports the stated maximum tested group size.
- The described task involved groups of up to five, which supports treating the larger-group claim as untested rather than demonstrated.
- The passage reports practical value and faster statement production but does not establish that those benefits persist at larger group sizes; the answer presents scalability only as a proposal.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
context
The authors see promise for wider use The authors describe the approach as time-efficient, fair, scalable, and better than human mediators on key measures. They say it may help people find agreement and support collective action in areas such as negotiations, conflict resolution, political discussion, and citizens’ assemblies.
Reports the authors’ general view that the method is scalable, while noting that this is potential rather than proof across settings.
AI can help humans find common ground in democratic deliberation
Read full source: AI can help humans find common ground in democratic deliberation (opens in a new tab) ↗Read source discussion: AI can help humans find common ground in democratic deliberation →
context
Time, cost, and group size The authors say citizens’ assemblies can take time and money to organize and attend, and discussion can involve only a limited number of people at once.
Provides background on limits of existing deliberation, but does not describe the method’s scaling basis or tested group sizes.
AI can help humans find common ground in democratic deliberation
Read full source: AI can help humans find common ground in democratic deliberation (opens in a new tab) ↗Read source discussion: AI can help humans find common ground in democratic deliberation →
context
Structured experiments The researchers report structured experiments involving 5,734 human participants in total.
Reports total participants across experiments, which does not establish the size of any deliberating group.
AI can help humans find common ground in democratic deliberation
Read full source: AI can help humans find common ground in democratic deliberation (opens in a new tab) ↗