cs.HCApr 7, 2026

AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent

Authors: Suyash Fulay, Prerna Ravi, Emily Kubin, Shrestha Mohanty, Michiel Bakker, Deb Roy

Organizations: Massachusetts Institute of Technology Cambridge, Massachusetts, USA · Oxford University Oxford, United Kingdom

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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.

Figures & tables

Figure 1. An overview of the different elements used in our study’s process (Detailed version in Appendix K )
Figure 2. Procedure for curating data for the visualization
Figure 3. Coefficients for visualization, AI interview, and their interaction (90% CI). Red: p < 0.1 ; solid markers: p < 0.05 .
Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Recommendation IDAccuracyCorrelationMAE
740.810.7917
750.810.6120
760.840.7716
Average0.820.7217
Appendix
Table 1. Accuracy, correlation, and mean absolute error (MAE) by recommendation.
ProposalAgainst MeanPro Mean
The federal minimum wage should be raised to $30 an hour.41.163.3
Companies should strongly prioritize hiring domestically before considering foreign applicants.44.067.8
Race and gender should be used in hiring decisions to combat inequality in the workplace.31.854.7
Appendix
Table 2. Pro and against means for sampled profiles.
StatementLow (0–33)Mid (33–66)High (66+)
The federal minimum wage should be raised to $30 an hour.51023
Companies should strongly prioritize hiring domestically before considering foreign applicants.748
Race and gender should be used in hiring decisions to combat inequality in the workplace.1798
Appendix
Table 3. Responses categorized by support levels for different policy statements.
DomainConceptCronbach’s α\alphaN items
Process LegitimacyAdherence0.935
Understood Decisions0.884
Trust0.783
Felt Heard0.553
Social CohesionLearn about others0.934
Respect0.875
Appendix
Table 4. Cronbach’s α\alpha for concepts grouped by domain.
 VisualizationInterviewInteraction 
CategoryOutcomeCoef.90% CIppCoef.90% CIppCoef.90% CIppRadj2R^{2}_{adj}
Process LegitimacyAdherence0.34*[0.04, 0.64]0.060.11[-0.20, 0.42]0.55-0.23[-0.66, 0.19]0.360.24
 Felt Heard0.29*[0.04, 0.54]0.060.51***[0.25, 0.76]0.001-0.20[-0.56, 0.15]0.340.21
 Trust0.71***[0.41, 1.01]<.0010.19[-0.11, 0.50]0.30-0.53**[-0.95, -0.11]0.040.13
 Understood Decisions0.77***[0.47, 1.07]<.0010.19[-0.12, 0.50]0.31-0.32[-0.74, 0.11]0.220.18
Appendix
Table 5. Concept outcomes associated with adherence to participatory processes across visualization and interview modes.
 VisualizationInterviewInteraction
CategoryOutcomeCoef.90% CIqqCoef.90% CIqqCoef.90% CIqq
AdherenceI would be willing to abide by the specific proposal decisions generated from this process.0.88[0.26, 1.49]0.100.43[-0.20, 1.06]0.81-0.84[-1.73, 0.03]0.50
 Generally, I would be willing to abide by proposal decisions from this process (on other issues).0.64[0.01, 1.26]0.100.38[-0.26, 1.03]0.81-0.69[-1.57, 0.16]0.83
 Even if I disagreed with certain proposal decisions, I would view them as being legitimately created.0.69[0.08, 1.29]0.120.04[-0.55, 0.63]0.93-0.11[-0.96, 0.75]0.83
 Even if I disagreed with certain proposal decisions, I would still respect them.0.60[0.03, 1.17]0.120.03[-0.55, 0.62]0.93-0.22[-1.03, 0.59]0.83
 Even if I disagreed with certain proposal decisions, I would still adhere to them.0.14[-0.44, 0.72]0.700.10[-0.50, 0.70]0.93-0.19[-1.01, 0.64]0.83
Appendix
Table 6. Item outcomes associated with adherence to participatory processes across visualization and interview modes.
 VisualizationInterviewInteraction 
CategoryOutcomeCoef.90% CIppCoef.90% CIppCoef.90% CIppRadj2R^{2}_{adj}
Social CohesionCommonalities0.40**[0.16, 0.64]0.010.04[-0.21, 0.29]0.79-0.03[-0.37, 0.31]0.870.23
 Inclusion of Self in Others0.48**[0.12, 0.84]0.030.11[-0.26, 0.48]0.63-0.18[-0.70, 0.33]0.560.11
 Feel Connected0.82***[0.48, 1.16]<< .0010.18[-0.17, 0.53]0.40-0.50*[-0.98, -0.02]0.090.22
 Willing to Interact-0.14[-0.49, 0.20]0.490.05[-0.30, 0.40]0.810.14[-0.35, 0.62]0.640.21
 Curious About Others-0.10[-0.46, 0.27]0.65-0.32[-0.70, 0.05]0.160.06[-0.46, 0.58]0.850.10
Appendix
Table 7. Concept outcomes associated with social cohesion across visualization and interview modes.
 VisualizationInterviewInteraction
CategoryOutcomeCoef.90% CIqqCoef.90% CIqqCoef.90% CIqq
CommonalitiesI probably have things in common with the other participants whose experiences and opinions helped to create these proposals.0.60**[0.16, 1.03]0.050.02[-0.43, 0.47]0.94-0.04[-0.58, 0.55]0.93
 I probably share values, opinions, or experiences with others who were part of this process.1.13***[0.66, 1.61]<.0010.16[-0.33, 0.64]0.85-0.17[-0.68, 0.50]0.93
 I probably have very little in common with other people who were part of this process.0.55[-0.10, 1.19]0.21-0.19[-0.85, 0.47]0.85-0.13[-1.01, 0.78]0.93
 I am reading all of the questions in this survey carefully.0.02[-0.10, 0.14]0.780.04[-0.08, 0.17]0.850.01[-0.16, 0.18]0.93
GeneralThink about yourself (Self) and the other participants in this study (Other)…0.89**[0.22, 1.57]0.030.20[-0.49, 0.89]0.63-0.34[-1.30, 0.62]0.56
Appendix
Table 8. Outcomes associated with perceptions of social cohesion across visualization and interview modes.
 VisualizationInterviewInteraction 
CategoryOutcomeCoef.90% CIppCoef.90% CIppCoef.90% CIppRadj2R^{2}_{adj}
Social CohesionLearning about topics0.93***[0.63, 1.23]0.000.47[0.01, 0.16]0.78-0.72**[-1.15, -0.29]0.010.28
 Stance certainty-0.13[-0.33, 0.07]0.270.10[-0.10, 0.39]0.300.09[-0.19, 0.36]0.600.33
 Change stance (self report)0.32*[0.02, 0.63]0.08-0.17[-0.48, 0.39]0.15-0.01[-0.44, 0.43]0.980.12
 Topic stance0.10[-0.07, 0.28]0.32-0.04[-0.22, 0.68]0.13-0.16[-0.40, 0.08]0.280.50
Appendix
Table 9. Concept outcomes associated with attitude change across visualization and interview modes.
 VisualizationInterviewInteraction
CategoryOutcomeCoef.90% CIqqCoef.90% CIqqCoef.90% CIqq
Learning about topicsCompared to before, I feel more informed about why people may have different beliefs on raising the minimum wage.1.84***[1.19, 2.49]0.001.13**[0.46, 1.79]0.03-1.47**[-2.39, -0.55]0.02
Learning about topicsI feel more informed about the topic of raising the minimum wage.1.71***[1.05, 2.38]0.000.80*[0.12, 1.48]0.06-1.41**[-2.36, -0.47]0.02
Learning about topicsThis exercise has taught me something about raising the minimum wage.1.62***[0.96, 2.27]0.000.80*[0.13, 1.47]0.06-1.05[-1.97, -0.12]0.07
Learning about topicsCompared to before, I feel more informed about why people may have different beliefs on hiring locals over international applicants.2.05***[1.42, 2.68]0.000.80*[0.16, 1.45]0.06-1.23*[-2.12, -0.34]0.03
Learning about topicsI feel more informed about the topic of hiring locals over international applicants.1.85***[1.20, 2.50]0.001.02**[0.35, 1.68]0.03-1.68**[-2.60, -0.76]0.01
Appendix
Table 10. Outcomes associated with attitude change across visualization and interview modes.
QuestionInstructionMax Sec
To start off, can you tell me a bit about your background? Where did you grow up, and what was it like? You can share anything about your family, your neighborhood, friends, or anything else you can think of.Learn as much as you can about the interviewee’s life experience, and their personal background. Be respectful but curious as you hear about their story. Try to get some specific and detailed experiences or memories about the interviewee’s life.30
Now I’d like to ask you about an important relationship in your life. Who is someone who has had a big impact on you? Can you tell me about your relationship with them—how you met, what they mean to you, and any important memories you’ve shared?Learn about a personally meaningful relationship. Encourage storytelling and emotional depth. Ask follow-ups to explore how this relationship shaped the interviewee’s identity, values, or perspective.30
Can you tell me about the work you do now? What’s your job like, and how do you feel about it day to day?Learn about what the interviewee does for a living. Be curious but respectful as you hear about their story. Follow up and ask what they like and dislike about their job.45
Generally, what could be done to make your work life better? You can discuss whatever you feel would make your life better, either now or in the past.Learn about what the interviewee thinks could be done to make their work life better. Be curious about their thoughts. Try to get some specific examples or ideas about how to improve their work life. If they need inspiration, you can give them some ideas but try not to bias them. If their answers are too brief or surface-level, probe further to understand their reasoning. You can even ask them what they would tell someone in-power like a lawmaker or employer.60
We’re now going to turn to the topic of the minimum wage. How would changes in the minimum wage (either raising or lowering it) affect you, your family, or anyone else you know personally?Learn about how the interviewee thinks changes in the minimum wage would affect them, their family, or anyone else they know personally. Be curious about their thoughts. Make sure you ask follow up questions to get more specific on how they would be impacted. If they say they would be negatively impacted, ask why they think that. If they do not think they would be impacted, prompt them to think about different people they interact with that may be impacted.60
What do you think is a fair minimum wage in your area? Why?Understand the interviewee’s thoughts on what a fair minimum wage is. Be curious and try to get some specific and detailed thoughts about the interviewee’s thoughts on what a fair minimum wage is. For example, you can ask them to think about rent, food, and other basic necessities in their area.45
Appendix
Table 14
Figure 4. The red values indicate significance at p < 0.1 , with solid markers indicating significance at p < 0.05 .
CategoryDistribution
Political OrientationVery Liberal (12%), Somewhat Liberal (8%), Liberal (20%), Moderate (16%), Somewhat Conservative (9%), Conservative (19%), Very Conservative (15%)
IncomeLess than 30,000(1530,000 (15%), 30,000–49,999(1549,999 (15%), 50,000–99,999(3799,999 (37%), 100,000 or more (31%), Prefer not to answer (1%)
EducationSome college but no degree (15%), Associate degree in college (2-year) (17%), Bachelor’s degree in college (4-year) (11%), Master’s degree (35%), Doctoral degree (19%), Professional degree (JD, MD) (1%), Prefer not to answer (2%)
EthnicityAsian (7%), Black (14%), Mixed (6%), White (69%), Other (2%), Unreported (3%)
Employment StatusFull-Time (50%), Part-Time (19%), Unemployed and seeking (8%), Not in paid work (10%), Due to start a new job (1%), Other (3%), Unreported (10%)
SexFemale (59%), Male (39%), Unreported (2%)
Appendix
Table 13. We report the breakdown of our sample of participants across various social and demographic categories.
DomainThemeDefinition
General ContextOverall impressions of the processInitial reactions, comfort, and general reflections on using the AI interviewer + platform
 Tool role, usability, & comprehensionReflections on overall tool usability, its role, and features
 Personal profile accuracy & agencyReactions to viewing their own profile
 Profile representationViews on how profiles were represented on the tool, including their positions on the 2-D plot
Social CohesionImpact of “hearing” othersImpact of voices via transcripts and audio recordings
 Openness to engagementDesire to interact further with other participants, and how
Appendix
Table 14. Interview themes and their definitions mapped to our study’s domains. The bolded themes are discussed in this paper for the purposes of the study.
Figure 5. STEP 0: AI Interviewer screen where the user has a back n forth conversation with the AI agent on their personal life as well as policy related experiences
Figure 6. STEP 1: Voting screen where the user views the proposal and votes on it through a Likert scale and free-response question
Figure 7. STEP 2a: Visualization showing the user where they stand relative to other study participants
Figure 8. STEP 2b: Right side panel that opens up when the user clicks on their own avatar from the previous screen. This shows them how their life stories and experiences relevant to the proposal get extracted and shown to other participants
Figure 9. STEP 3a: Visualization showing three featured profiles of participants from across the spectrum on predicted support for the proposal
Figure 10. STEP 3b: Right side panel that opens up when the user clicks on any of the featured avatars from the previous screen. This allows the user to listen and react to the participant’s life and policy-related experiences through with audio clips and reflection scaffolds
Figure 11. STEP 3c: Visualization showing all avatars on the spectrum. The user can optionally explore these after the previous steps
Figure 12. STEP 4: Decision page showing the user how the participant group voted summatively and if the proposal passed/failed. The user also gives feedback on the decision via likert scales below
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