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.

翻译：政治对话常被提议作为缓解政治极化的手段，但其有效性仍存在矛盾。我们认为，这种矛盾部分反映了政治接触中被忽视的特征：党派成员对对话的预期。我们假设，当对话挑战了党派身份与政策立场之间的预期联系时，最能降低政治极化。我们通过一个2x2实验检验这一假设，其中1,983名美国成年人参与了与AI聊天机器人的结构化对话，该聊天机器人的党派身份和政策立场被独立操控。研究发现，挑战预期的对话——参与者与持不同意见的党内成员或持相同意见的党外成员对话——能有效降低情感极化和议题极化。尽管这些效果并未伴随参与者自身政策立场的显著变化，但后续调查显示，大多数效果在一个月后消失。有趣的是，这些对话保持了或改善了客观审议指标，但参与者对其满意度较低。我们的研究确定预期违背是一种尚未充分探索的去极化机制。同时，我们的结果也展示了对话式AI作为研究干预措施以缓解党派分歧的可扩展实验方法的潜力与局限。