Challenging Partisan Expectations Reduces Political Polarization

2026-06-14

AI Summary

This study addresses the limited efficacy of political dialogue in mitigating polarization, often stemming from overlooked expectations partisans hold about their interlocutors. Through a 2×2 randomized controlled experiment manipulating both the partisan identity and policy stance of an AI chatbot, the research identifies “expectancy violation”—defined as engaging with either an in-group member who holds opposing views or an out-group member who shares one’s views—as a key mechanism for reducing both affective and issue-based polarization. Although such interactions did not shift participants’ policy positions and their effects decayed after one month, they significantly enhanced the quality of deliberative reasoning. These findings underscore both the promise and limitations of AI-mediated dialogue as a scalable intervention for depolarization.

Abstract

Political conversations are often proposed as a remedy for political polarization, yet their effectiveness remains inconsistent. We argue that this inconsistency partly reflects a neglected feature of political contact: the expectations partisans bring to these encounters. We hypothesize that conversations should reduce political polarization the most when they violate the expected link between partisan identity and issue position. We test this hypothesis in a 2x2 experiment in which 1,983 U.S. adults engaged in structured conversations with an AI chatbot whose presented partisan identity and policy stance were independently manipulated. We find that expectation-challenging conversations in which participants talk with a disagreeing ingroup member or an agreeing outgroup member are effective in reducing affective and issue polarization. Although these effects emerge without meaningful shifts in participants' own policy positions, a follow-up survey shows that most effects disappear over one month. Interestingly, these conversations maintain or improve objective measures of deliberation but are experienced as less satisfying by participants. Our findings identify expectation violation as an underexplored depolarization mechanism. Our results also demonstrate the promises and limitations of how conversational AI can serve as a scalable method for experimentally studying interventions to mitigating partisan divides.

Problem

• political polarization
• partisan expectations
• expectation violation
• affective polarization
• issue polarization

Innovation

• expectation violation
• political polarization
• conversational AI
• partisan identity
• deliberation