THE CIVIC MACHINE.

In everyday terms: this study asks whether AI can help a group write a statement people recognize as common ground. Approving the statement is different from agreeing on a policy or giving someone the power to decide it.

Deliberation is an exchange of reasons about a shared decision. A mediator helps people understand positions and formulate something they can consider together. Common ground can concern a shared purpose, a conditional compromise or an accurate account of unresolved disagreement. It does not require every person to want the same policy.

Consider a separate, invented museum consultation. One visitor wants more late openings. Another wants quiet daytime access. Both might accept a statement asking the museum to widen access while protecting a quiet period, even though they disagree about the timetable. The statement contributes criteria for evaluating schedules; it does not itself choose one. To judge that contribution, compare the visitors’ submissions with the actual words they endorsed.

A drafting procedure also has an objective. Predicting which text will win endorsement can help a group find language it recognizes, but endorsement is a particular criterion. It is not identical to factual accuracy, equal influence over a decision or protection against a severe cost. An institution must explain why it uses that criterion and what other requirements govern the eventual choice.

Tessler and colleagues’ purpose-built system generated candidate statements from private opinions, selected using predicted preferences and revised after critiques. Participants preferred AI statements over statements from trained ordinary participants. The research also examined viewpoint movement and minority contributions in text. Those are related questions with different evidence: liking a statement, moving toward a stance and having a concern appear in wording are not interchangeable outcomes. Here, minority text weight means a model estimate of minority positions represented in the statement, not votes or power to nominate or adopt a policy.

The politically difficult finding is that minority contributions could remain visible while attitudes shifted more often toward the initial majority overall. The text-position analysis found proportionate minority weight in first statements and greater weight after revision. That measure concerns textual positions; it does not demonstrate that minorities had equal agenda power or that anyone was protected from a material loss. Learning from new reasons and accommodating the group can be rival accounts of an attitude shift. The reported results do not settle every mechanism.

Return to the museum example. A summary could faithfully include quiet access while only one schedule is allowed onto the decision agenda. A visitor may change their preference and still regard the rule as unfair, or lose the timetable vote while accepting the procedure. Ask separately about representation, influence, consequences and authority. The study can inform the design of a drafting step; the institution must defend its broader rule for a binding decision. In a separate invented example, a visitor may prefer Sunday opening yet accept a previously announced selection rule after a weekday schedule wins. Ask about the preferred schedule and acceptance of the rule separately.

How the researchers studied it

UK adults submitted opinions, rated candidate common-ground statements and provided critiques for revision. The system optimized predicted endorsement. The human comparison used trained, incentivized ordinary participants, not professional mediators.

Selection occurred at two levels. Inside the HM, predicted personal rankings selected a generated output. Participants then ranked the displayed outputs themselves to select the statement they would critique. The human-versus-AI comparison omitted the critique phase; separate experiments tested critique and revision.

What they found

AI statements won 56% of preferences against 44% for human statements. Revised texts were favored. Attitude movement leaned toward initial majorities overall; text-position analysis found minority contributions and increased minority weight after critique.

Where the claim stops

These tested drafting and deliberation outcomes do not establish factual verification, equal decision influence, universal fairness or municipal authority. Textual representation and attitude movement require separate interpretation.

Read the procedure and human comparison; RQ2 on viewpoint change; RQ3 on minority text and majority-aligned movement; then the Discussion’s limitations.

In everyday terms: this study asks whether an AI conversation can move people toward a candidate or position it was told to support. A change in a survey answer is different from an observed vote.

Persuasion is an attempt to change judgment through reasons or appeals. It may be disclosed and legitimate. Directional advocacy has a specified target, such as support for one candidate. A common-ground drafting system seeks an acceptable group statement. Both jobs can alter views; a view change alone does not tell us whether a process was truthful, representative or democratically authorized.

In the unrelated museum example, compare an assistant asked to represent visitors’ reasons with one asked to increase support for late openings. Both might acknowledge concerns respectfully. The tone cannot reveal the objective. Inspect the commissioning instruction, material selected, alternatives discussed and response measured. A statement’s accuracy and the visitor’s preferred schedule remain separate questions.

Lin and colleagues used assigned advocacy in survey dialogues concerning candidates and a ballot measure. Their national experiments compared opposing advocates; they were not simply an advocate versus no conversation. The measured changes concerned attitudes and reported intentions. An attitude-scale movement is not observed voting, a vote-share change or an election result.

A before-and-after change can also reflect repeated questioning, unrelated information or selection into completing a conversation. A controlled comparison holds the defined outcome and relevant conditions comparable while varying the assigned feature. Random allocation helps address prior differences under its assumptions; it does not make every later generalization automatic.

The study’s randomized strategy changes are especially useful for mechanism reasoning. Removing facts and evidence weakened persuasion in Canada and Poland; removing personalization in Poland did not clearly reduce it. An observed link between a strategy and attitude change is weaker causal evidence than deliberately varying that strategy. Some claims were inaccurate, but this is not a demonstration that falsehood caused the influence.

Use this research to pose a precise institutional question. Who chose the system’s objective, who benefits from movement toward the target, and which outcome should a reviewer evaluate? Knowing that a system is AI or barring one kind of personal data does not settle those questions. Neither article chooses a legitimate museum timetable—or a legitimate rule for any other institution—for us.

How the researchers studied it

Lin and colleagues randomly assigned survey participants to candidate-advocating AI dialogues in the US, Canada, and Poland, and to conditions concerning a Massachusetts ballot measure. They measured attitudes and reported voting intentions.

What they found

Assigned advocacy changed candidate preferences. Removing facts and evidence reduced persuasion in Canada and Poland; removing personalization in Poland did not significantly reduce it. Some AI claims were inaccurate. Part of the US attitude change remained at follow-up.

Where the claim stops

These experiments establish influence under their survey conditions, not observed ballot changes or election outcomes. They do not show that all persuasion depends on personal data, that falsehood caused the effects, or that a common-ground mediator has the same objective.

Read Effects of the human–AI dialogue; Testing mechanisms in Canada and Poland; and Discussion, including the field-deployment limits.