Contribution to the handbook
Supporting · Assessed 2026-10-28
The procedure offers a concrete example of participants discussing policy questions, critiquing and ranking proposed statements, and rating positions before and after deliberation. This can inform participation design, but the question is mainly descriptive and does not establish effects or suitability for governance.
Does this challenge the goal?
In-scope gap
The question supplies detail about a deliberation procedure and its measures, within the accepted goal of exploring citizen participation. It does not contest the goal or beneficiary boundary.
The evidence describes one study task; it does not establish that the procedure is suitable for governance.
Why this category?
- Concrete participation procedure relevant as background to citizen deliberation
- Limited transfer from a study task to governance design
The records share one source root and are AI interpretations of the same article, not independent evidence. Supplementary materials are not included, and the evidence does not show whether the task changed participants’ views.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
- What did participants do in the main deliberation task?
- The AI revised statements using feedback
- Selecting a group statement
- Participants ranked and critiqued statements
- Revisions used critiques and earlier views
- Participants rated and ranked statements
- Revising statements from critiques
- Statements aimed at shared ground
- Predicted rankings selected a winner
- Divisive UK policy questions
- Human deliberation task
- A typical discussion session
- A private group mediator
Making and interpreting laws →
Tests, stop conditions and unresolved work →
Read the exploration goalDescribe the main task’s group discussion and mediator-related activities, including the questions participants addressed.
What we can say so far
Groups of typically about five discussed three questions in sequence, in sessions lasting about an hour; a round on one question lasted about 15 minutes. Participants privately wrote a short account of their views, usually including a position and a reason, and rated their initial positions and how important each question was to them. The mediator privately collected those views, generated candidate group statements, and used participants’ ratings or predicted rankings to select a statement for the group to consider. Participants rated and ranked statements, critiqued the leading statement, and the system used critiques together with the initial views and statement to generate revised candidates. Participants ranked the revised candidates and compared the initial and revised winners. They also answered a brief survey about whether the process affected their views and rated their positions and question importance again after the rounds. The reported topics included whether to lower the voting age to 16 and whether to privatize the NHS. The evidence identifies three questions but names only these two; it does not provide the third question or the complete set of exact prompts.
What remains open
- The third question and the complete set of exact question prompts are not supplied.
- The evidence does not specify how the mediator handled conflicting critiques or participants who rejected a proposed statement.
Conditions and limits
- This is a description of the reported study procedure, not evidence that the process changed views or produced acceptable outcomes.
- The named policy questions are examples; the evidence does not establish that they are the complete set of prompts.
- The accounts are interpretations of the same article source, not independent verification; supplementary materials are not included.
Objections to consider
- The evidence describes private view collection and proposed statements but does not explain what participants did if they rejected a proposal.
- The evidence describes critique-based revision but does not explain how conflicting critiques were handled or whether every critique was incorporated.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
supports
The AI revised statements using feedback The AI helper drafted a group statement from participants’ opinions, then revised it using their critiques. The design drew on Jürgen Habermas’s theory of communicative action; across successive rounds, researchers used supervised fine-tuning and reward modeling to improve the system, with group approval as the stated goal.
Describe when participants rated their positions and the importance of the questions.
What we can say so far
The reported procedure had participants rate their initial positions and the importance of each question, discuss three questions, and then rate their positions and question importance again. The article is also reported to specify that participants rated their positions immediately before and after the full set of deliberation rounds.
What remains open
No material gap is recorded within this part’s current scope.
Conditions and limits
- This is a report of the procedure in the readable article text; supplementary materials were not included.
- The evidence consists of AI interpretations of shared source material, not independent verification.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
supports
Participants ranked and critiqued statements Participants privately wrote a short paragraph about their opinion; they typically gave both their position and a reason. The AI helper generated candidate group statements for participants to rate and rank. After the top-ranked statement was critiqued, the AI generated revised candidates for another ranking. Participants compared the initial and revised winners and answered a brief survey about whether the process affected their views. They also rated their positions on the questions immediately before and after the full set of deliberation rounds.
Specifies that position ratings occurred immediately before and after the full set of deliberation rounds.
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 →
supports
Human deliberation task Groups of up to five people rated their initial positions and how important each question was to them. They discussed three questions, then rated their positions and question importance again.
Reports initial and repeated ratings of both positions and question importance around discussion of the three questions.