Scholars and activists have raised alarm about affective polarization---voters' increasing dislike for supporters of opposing political parties---and its potential negative implications for democracy. Organizations, activists, and scholars have identified cross-partisan conversations as a promising paradigm for reducing affective polarization and, in turn, bolstering democratic accountability. However, existing theory and empirical work remains ambiguous. We argue that cross-partisan conversations have potential to reduce intergroup prejudices, but that such one-shot interactions are likely to have short-term effects that decay, would be circumscribed within the interpersonal domain and not extend to democratic attitudes, and would be conditional on topic, diminishing if the conversations dwell on group differences. We support this argument with results from two unique experiments where we paired outpartisan strangers in real time to discuss randomly assigned topics face-to-face over video calls. In Study 1, we found that non-political conversations between outpartisans dramatically decreased affective polarization, reversing over two decades' worth of increases. However, these impacts decayed completely in a follow-up survey. Moreover, the conversations had no effect on outcomes related to democratic accountability, such as support for outpartisan politicians. Study 2 replicated Study 1's results in a more representative sample and included conversations about group differences (i.e., politics). We again found large effects of non-political cross-partisan conversations on affective polarization, but that conversations about group differences had no effects. All conversations were again ineffective at changing democratic attitudes. Our results support our argument regarding the conditional, short-term, and circumscribed effects of cross-partisan conversations.

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Many efforts to persuade others politically employ interpersonal conversations. A recurring question is whether participants in such conversations are more readily persuaded by others who share their demographic characteristics (e.g., race). Echoing broader concerns that Americans have difficulties communicating across differences, research finds that individuals perceive demographically similar people as more trustworthy, suggesting shared demographics could facilitate persuasion. In a survey of practitioners and scholars, we find many share these expectations. However, dual-process theories suggest that messenger attributes are typically peripheral cues which should not influence persuasion when individuals are effortfully thinking, such as during interpersonal conversations. Supporting this view, we analyze data from eight experiments on interpersonal conversations across four topics (total N = 6,139) and find that shared demographics (age, gender, or race) do not increase their effects. These results are encouraging for the scalability of interpersonal conversation interventions, and suggest Americans are capable of persuading each other across difference.

Will people redefine what they see as essential to the human experience as the human species loses ground to Artificial Intelligence (AI) on such attributes as logic and language? Four studies using U.S. samples (N = 2,101) test this possibility. Study 1 supports a two-part classification of human attributes that we then use in subsequent studies. Studies 2a-2c demonstrate that, compared to reading a control prime about trees, reading a treatment prime about AI progress leads participants to view those attributes not shared with AI (e.g., humor and having desires) as more essential to being human. We found no evidence that this exposure leads to devaluation of those attributes shared with AI (e.g., logic and language). This research suggests that as AI becomes ubiquitous and increasingly capable, conceptions of human nature may shift in reaction to value what makes humans unique.