# AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent

arXiv: https://arxiv.org/abs/2604.05368 · HTML: https://mlsanity.com/paper/2604.05368/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 96 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 14 of 14)

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- [Language Agents as Digital Representatives in Collective Decision-Making](https://mlsanity.com/paper/2502.09369/) (2025)
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- [Opening up Minds with Argumentative Dialogues](https://mlsanity.com/paper/2301.06400/) (2023)
- [Adaptive political surveys and GPT-4: Tackling the cold start problem with simulated user interactions](https://mlsanity.com/paper/2503.09311/) (2025)
- ["Hi. I'm Molly, Your Virtual Interviewer!" -- Exploring the Impact of Race and Gender in AI-powered Virtual Interview Experiences](https://mlsanity.com/paper/2408.14159/) (2024)
- [Enhancing Deliberativeness: Evaluating the Impact of Multimodal Reflection Nudges](https://mlsanity.com/paper/2502.03862/) (2025)
- [BoundarEase: Fostering Constructive Community Engagement to Inform More Equitable Student Assignment Policies](https://mlsanity.com/paper/2503.08543/) (2025)

## Abstract (arXiv)

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.