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arxiv: 2604.05368 · v1 · submitted 2026-04-07 · 💻 cs.HC · cs.AI

Recognition: no theorem link

AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent

Suyash Fulay , Prerna Ravi , Emily Kubin , Shrestha Mohanty , Michiel Bakker , Deb Roy
Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:55 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords AIcollective decision-makingprocedural legitimacylosers' consentvisualizationtrusthuman-computer interactionpolicy experiences
0
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The pith

An AI visualization displaying personal experiences alongside policy predictions can raise legitimacy and trust even for participants whose preferred outcomes are rejected.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether AI can strengthen procedural legitimacy in collective decisions by eliciting diverse personal experiences and making them visible to participants. It asks if seeing these experiences improves trust, understanding, and acceptance when decisions go against one's own preferences. Researchers built an AI interviewer to collect stories and paired it with an interactive display of predicted support levels and the collected experiences. A controlled experiment with 181 people found that using the visualization produced measurable gains in perceived fairness and perspective-taking despite uniform exposure to unfavorable results. This line of work addresses the risk that scaled AI decisions erode consent among those who lose out.

Core claim

We built a system that uses a semi-structured AI interviewer to elicit personal experiences on policy topics and an interactive visualization that displays predicted policy support alongside those voiced experiences. In a randomized experiment (n = 181), interacting with the visualization increased perceived legitimacy, trust in outcomes, and understanding of others' perspectives, even though all participants encountered decisions that went against their stated preferences.

What carries the argument

The interactive visualization that pairs predicted levels of policy support with the personal experiences collected by the semi-structured AI interviewer.

If this is right

  • AI-assisted collective decision tools can raise perceived fairness by surfacing opposing personal experiences without requiring preference change.
  • Visual exposure to others' viewpoints can improve understanding and trust in the process even when the final outcome is disliked.
  • Procedural legitimacy in scaled decisions depends partly on making the diversity of participant experiences legible to everyone involved.
  • Design efforts in democratic AI should treat losers' consent as a measurable outcome alongside efficiency and accuracy.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Deploying similar visualizations in public deliberation platforms could lower post-decision conflict if the elicited experiences are representative.
  • The approach might complement existing voting or deliberation systems by adding an empathy layer before final tallies are taken.
  • Effects could weaken if the AI interviewer produces low-quality or biased experience summaries.
  • Testing the tool in high-stakes settings such as local budgeting or regulatory decisions would reveal whether lab gains persist.

Load-bearing premise

Short-term self-reported gains in legitimacy from a single controlled session with the visualization will carry over into sustained real-world acceptance of collective decisions.

What would settle it

A longitudinal field study that tracks whether participants who used the tool actually comply with or protest an unfavorable policy decision weeks or months later.

Figures

Figures reproduced from arXiv: 2604.05368 by Deb Roy, Emily Kubin, Michiel Bakker, Prerna Ravi, Shrestha Mohanty, Suyash Fulay.

Figure 1
Figure 1. Figure 1: An overview of the different elements used in our study’s process (Detailed version in Appendix K) [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Procedure for curating data for the visualization [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Coefficients for visualization, AI interview, and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The red values indicate significance at p < 0.1, with solid markers indicating significance at p < 0.05. I Participant Demographics Category Distribution Political Orientation Very Liberal (12%), Somewhat Liberal (8%), Liberal (20%), Moderate (16%), Somewhat Conservative (9%), Conservative (19%), Very Conservative (15%) Income Less than $30,000 (15%), $30,000–$49,999 (15%), $50,000–$99,999 (37%), $100,000 … view at source ↗
Figure 5
Figure 5. Figure 5: STEP 0: AI Interviewer screen where the user has a back n forth conversation with the AI agent on their personal life [PITH_FULL_IMAGE:figures/full_fig_p024_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: STEP 1: Voting screen where the user views the proposal and votes on it through a Likert scale and free-response [PITH_FULL_IMAGE:figures/full_fig_p024_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: STEP 2a: Visualization showing the user where they stand relative to other study participants [PITH_FULL_IMAGE:figures/full_fig_p025_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: STEP 2b: Right side panel that opens up when the user clicks on their own avatar from the previous screen. This [PITH_FULL_IMAGE:figures/full_fig_p025_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: STEP 3a: Visualization showing three featured profiles of participants from across the spectrum on predicted support [PITH_FULL_IMAGE:figures/full_fig_p026_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: STEP 3b: Right side panel that opens up when the user clicks on any of the featured avatars from the previous screen. [PITH_FULL_IMAGE:figures/full_fig_p026_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: STEP 3c: Visualization showing all avatars on the spectrum. The user can optionally explore these after the previous [PITH_FULL_IMAGE:figures/full_fig_p027_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: STEP 4: Decision page showing the user how the participant group voted summatively and if the proposal passed/failed. [PITH_FULL_IMAGE:figures/full_fig_p027_12.png] view at source ↗
read the original abstract

AI is increasingly used to scale collective decision-making, but far less attention has been paid to how such systems can support procedural legitimacy, particularly the conditions shaping losers' consent: whether participants who do not get their preferred outcome still accept it as fair. We ask: (1) how can AI help ground collective decisions in participants' different experiences and beliefs, and (2) whether exposure to these experiences can increase trust, understanding, and social cohesion even when people disagree with the outcome. We built a system that uses a semi-structured AI interviewer to elicit personal experiences on policy topics and an interactive visualization that displays predicted policy support alongside those voiced experiences. In a randomized experiment (n = 181), interacting with the visualization increased perceived legitimacy, trust in outcomes, and understanding of others' perspectives, even though all participants encountered decisions that went against their stated preferences. Our hope is that the design and evaluation of this tool spurs future researchers to focus on how AI can help not only achieve scale and efficiency in democratic processes, but also increase trust and connection between participants.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper describes an AI system using a semi-structured AI interviewer to elicit participants' personal experiences on policy topics, paired with an interactive visualization showing predicted policy support alongside those experiences. It reports results from a randomized controlled experiment (n=181) claiming that interaction with the visualization increased perceived legitimacy, trust in outcomes, and understanding of others' perspectives, even among participants whose preferences were not reflected in the final decision.

Significance. If the experimental results hold under more rigorous validation, the work could meaningfully advance HCI research on AI-supported collective decision-making by providing evidence that targeted visualizations of diverse experiences can bolster procedural legitimacy and losers' consent. This addresses a key gap in scaling democratic processes while preserving social cohesion, and the empirical focus on adverse-outcome scenarios offers a falsifiable starting point for future studies.

major comments (2)
  1. [Abstract and Experiment] Abstract and Experiment section: The central claim rests on positive effects from the n=181 randomized trial, yet no details are provided on the precise outcome measures (e.g., exact survey items or scales for legitimacy, trust, and perspective-taking), statistical tests performed, effect sizes, power analysis, or controls for demand characteristics and baseline differences. Without these, the reported increases cannot be properly evaluated for robustness or replicability.
  2. [Results and Discussion] Results and Discussion sections: The evaluation uses only immediate post-interaction self-reports. No behavioral measures of actual consent (such as willingness to comply with or publicly endorse an adverse outcome in a follow-on task) or delayed re-assessment are described. This is load-bearing for the paper's broader argument that the system strengthens losers' consent, as transient perceptions may not map to durable acceptance in real collective decisions.
minor comments (2)
  1. [System Description] System Description: Additional specifics on the AI model, prompt templates for the interviewer, and how predictions of policy support are computed would improve reproducibility.
  2. [Figures] Figures: The visualization examples could include clearer annotations or legends to show how individual experiences are aggregated and displayed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments on our manuscript. We address each major comment below and outline the revisions we will make to improve transparency and contextualize the scope of our findings.

read point-by-point responses

  1. Referee: [Abstract and Experiment] Abstract and Experiment section: The central claim rests on positive effects from the n=181 randomized trial, yet no details are provided on the precise outcome measures (e.g., exact survey items or scales for legitimacy, trust, and perspective-taking), statistical tests performed, effect sizes, power analysis, or controls for demand characteristics and baseline differences. Without these, the reported increases cannot be properly evaluated for robustness or replicability.

    Authors: We agree that the manuscript requires more explicit reporting of these methodological and statistical details to support evaluation and replicability. In the revised version we will expand the Experiment section with a table of all survey items and their exact wording and response scales for perceived legitimacy, trust in outcomes, and perspective-taking. We will also report the full statistical tests (including any ANOVA or regression models), effect sizes, a power analysis, and our procedures for checking baseline equivalence across conditions and minimizing demand characteristics through neutral instructions and cover stories. revision: yes

  2. Referee: [Results and Discussion] Results and Discussion sections: The evaluation uses only immediate post-interaction self-reports. No behavioral measures of actual consent (such as willingness to comply with or publicly endorse an adverse outcome in a follow-on task) or delayed re-assessment are described. This is load-bearing for the paper's broader argument that the system strengthens losers' consent, as transient perceptions may not map to durable acceptance in real collective decisions.

    Authors: We acknowledge that the study relies exclusively on immediate self-report measures collected after the interaction. This design was chosen to isolate the causal effect of the visualization on initial perceptions in a controlled, single-session setting. We recognize that behavioral measures and delayed assessments would provide stronger evidence for durable losers' consent. In the revised Discussion we will add an explicit limitations paragraph noting this scope and will outline concrete directions for future work that could incorporate behavioral tasks (e.g., willingness to publicly endorse or comply with an adverse decision) and longitudinal follow-ups. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical randomized experiment with no derivations or fitted predictions

full rationale

The paper reports building an AI interviewer and visualization tool, then presents results from a randomized controlled trial (n=181) on self-reported legitimacy, trust, and perspective-taking. No equations, parameters, or theoretical derivations appear in the provided text. Claims rest directly on experimental outcomes rather than any self-referential construction, fitted inputs renamed as predictions, or load-bearing self-citations. The design is self-contained against external benchmarks as an empirical study; the skeptic's concern about lacking behavioral proxies addresses validity, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of survey-based measures of legitimacy and the assumption that the AI system elicits authentic experiences without introducing bias; these are domain assumptions rather than derived results.

axioms (1)
  • domain assumption Perceived legitimacy, trust in outcomes, and understanding of others' perspectives can be reliably captured via self-report surveys administered immediately after a short interaction.
    The study uses these as primary outcomes without additional validation or behavioral measures mentioned.

pith-pipeline@v0.9.0 · 5499 in / 1243 out tokens · 42567 ms · 2026-05-10T19:55:49.824177+00:00 · methodology

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