Synthetic Contact with AI Reduces Cross-Partisan Animosity

Abstract. Americans’ warmth toward members of the opposing political party has fallen sharply over the past three decades—yet meaningful cross-partisan contact remains scarce, in part because people actively avoid it. Across five preregistered studies (total N = 3,960 U.S. partisans), we test whether brief conversations with AI chatbots representing the political outgroup can substitute for the contact people shun. Synthetic contact first lowers the barrier to entry: partisans would endure almost twice as long contemplating their own mortality to avoid a human outgroup partner as an AI one. These conversations then correct the misperceptions that fuel division. At baseline, Democrats placed Republicans more than a standard deviation past their actual position on environmental consumption attitudes—enough to flip the average Republican from supportive to opposed—and a single ten-minute conversation with an outgroup chatbot corrected those beliefs and warmed affect in a within-person study of both parties. A three-arm experiment ruled out pure engagement and sociality as drivers. Synthetic contact also moved behavior, in a sample of both parties and on a more affectively charged issue: participants who spoke with an outgroup bot about immigration were six percentage points more likely than controls to choose to have a real conversation with a partisan from the other side. A final study tested whether these gains last: the warmth effect replicated immediately in a new sample; most of it faded within a week, with a small residual concentrated among the most extreme partisans. Analyzing conversation content showed that information, more than friendliness, distinguishes outgroup bots from control chatbots. Together, these findings establish synthetic contact as a scalable, behaviorally consequential, and—unlike face-to-face contact—widely acceptable form of cross-partisan engagement.

Americans increasingly view political opponents with suspicion and dislike. Warmth toward the political outgroup has fallen steadily over the past three decades.1 This animosity is only one face of polarization.

Introduction. A chatbot prompted to represent the political outgroup stands in for a real interlocutor, available on demand. We use the term synthetic contact for conversations of this kind. Because no real person sits on the other side, partisans can engage the views they avoid without the threat that makes contact aversive in person and backfire online. The possibility that AI might improve intergroup relations has been raised conceptually: Hermann et al.6 propose that AI agents could reduce prejudice if engineered to be counter-stereotypical, deliberately built to contradict the outgroup’s negative stereotype. We test a more minimal version: rather than engineering the bot to defy stereotypes, we prompt it to represent a typical outgroup member. If partisan stereotypes are exaggerated, then even an accurate portrayal should contradict them, and we test whether that alone is enough to reduce prejudice.

Synthetic contact is also newly feasible, and several lines of evidence suggest it could work. Large language models can generate realistic representations of diverse political viewpoints,20,21 and participants often engage with them as they would a human conversation partner;22,23 meta-analytic evidence already shows that digitally mediated intergroup contact reduces prejudice.24 Recent work also shows that people perceive AI sources as less biased, more informative, and less persuasively intended than human sources, which increases receptiveness to opposing views.25 People are not only open to LLMs but persuaded by them: brief LLM dialogue can durably reduce conspiracy beliefs,26 shift candidate preferences in real elections,27 and outpersuade humans in head-to-head debate.28 But persuasive power cuts both ways. Large language models trained on internet text exhibit systematic political leanings,29,30 so a bot prompted to represent the political outgroup may portray its positions in caricatured rather than calibrated form. If so, its persuasive force would entrench partisan misperceptions rather than correct them—reinforcing the very stereotypes that contact is meant to dissolve.

Here, we test across five preregistered studies whether brief synthetic contact is acceptable, whether it corrects misperceptions and warms cross-partisan affect, and whether it moves a costly behavioral choice (Table 1 summarizes the design, sample, and primary outcome of each study). We begin with an incentivecompatible aversion experiment that trades off crosspartisan conversation against an aversive mortalityreflection task, quantifying how much more willing partisans are to engage with an AI outgroup partner than with a human one. A within-person study with Democrats and Republicans then tests whether a single ten-minute conversation with an outgroup-representing chatbot objectively corrects misperceptions and warms cross-partisan affect. A three-arm experiment compares synthetic contact against an active chat control (an equally polite AI conversation about an apolitical topic) and a non-social game control, isolating the causal contribution of outgroup-specific content. A two-arm behavioral experiment asks whether synthetic contact shifts behavior, not just attitudes: whether participants who just spoke with an outgroup bot are more willing to enter a real cross-partisan conversation than control participants. A longitudinal experiment tests whether brief synthetic contact produces durable attitude change one week later. Finally, we code the content of every bot conversation to ask which mechanism sets outgroup bots apart from controls—stereotype-disconfirming information (a cognitive route) or warmth and empathy (an affective route).11 Across these studies we deliberately varied the conversation topic: the misperception and warmth studies used environmental consumption attitudes, where a validated scale (the GREEN measure,31) quantifies misperception item by item, while the two incentivecompatible studies with real behavioral stakes used immigration, a more affectively charged identity issue and a harder test for contact. Convergent effects across both issues suggest that the effect is not specific to any single topic.

Related work. Intergroup contact offers one of the most robust solutions to this animosity. Since Allport,7 decades of research have established that positive interactions between members of different groups reduce prejudice and increase mutual understanding,8,9 though the strength of this evidence has been debated.10 Researchers still debate why contact works. A classic meta-analysis11 of over 500 effects points to three mediators: contact builds knowledge of the outgroup, lowers intergroup anxiety, and increases empathy. Notably, they find that the two affective routes, anxiety and empathy, outweigh the effects of gaining knowledge.

Recent work extends this framework to politics: bringing Democrats and Republicans together for crossparty discussion reduces affective polarization.12 Explicitly debating partisan disagreements, however, does not reliably help—Santoro and Broockman13 found that outpartisans who discussed a shared experience (their “perfect day”) grew less polarized, whereas those who debated their disagreements did not. lines of evidence suggest it could work. Large language models can generate realistic representations of diverse political viewpoints,20,21 and participants often engage with them as they would a human conversation partner;22,23 meta-analytic evidence already shows that digitally mediated intergroup contact reduces prejudice.24 Recent work also shows that people perceive AI sources as less biased, more informative, and less persuasively intended than human sources, which increases receptiveness to opposing views.25 People are not only open to LLMs but persuaded by them: brief LLM dialogue can durably reduce conspiracy beliefs,26 shift candidate preferences in real elections,27 and outpersuade humans in head-to-head debate.28 But persuasive power cuts both ways. Large language models trained on internet text exhibit systematic political leanings,29,30 so a bot prompted to represent the political outgroup may portray its positions in caricatured rather than calibrated form. If so, its persuasive force would entrench partisan misperceptions rather than correct them—reinforcing the very stereotypes that contact is meant to dissolve.

Method. Coding scheme. Each conversation was scored on four theoretically motivated dimensions on a 1–5 scale: two indexing the cognitive route to prejudice reduction—stereotype-disconfirming substance and informational specificity—and two indexing the affective route—empathy and friendliness.6,11 To block crossdimension halo bias, each dimension was coded in a separate API call (GPT-5.4-mini) with no information about the other dimensions; the model wrote onesentence reasoning before scoring.

Discussion. An AI partner halves the aversion to cross-partisan conversation Before synthetic contact can reduce polarization, partisans have to agree to it. In Study 1 (Preregistered, AsPredicted #286,575, N = 608), we measured how much of an aversive experience they would endure to avoid an outgroup conversation, and whether an AI partner lowers that price. Participants made repeated forced choices between three minutes of conversation about immigration with a member of the political outgroup and an adjustable duration X of reflection on one’s own mortality (a deliberately aversive task). The duration X was raised whenever a participant picked the conversation and lowered whenever they picked mortality reflection, homing in on the duration at which each participant was indifferent between the two. Only the conversation partner differed between conditions: in the human condition, participants weighed mortality reflection against three minutes with a live participant from the opposing party, whereas in the bot condi- DRAFT — NOT PEER REVIEWED Fig. 2. A single ten-minute conversation corrects misperceptions of the outgroup and warms attitudes toward it. Color denotes the participant party (blue = Democrats, red = Republicans). (A) Baseline misperceptions: each party’s estimate of the outgroup, the outgroup’s actual attitudes, and the bot’s position on the six environmental items. Democrats sharply underestimate Republican environmental concern, and the bot sits closer to real Republicans than Democrats’ estimate does. (B) Belief accuracy and (C) outgroup warmth, pre- and post-chat, by party; both improved after the conversation, and the size of belief correction predicts the size of warmth gain. Error bars are ±1 SE. such misperceptions may both warm cross-partisan affect and—over time—erode the very aversion that limits face-to-face contact. We now turn to whether brief AI conversations can correct these misperceptions, durably warm cross-partisan affect, and change behavior to make human intergroup contact more likely.

Partisans misperceive each other In Study 2 (Preregistered, AsPredicted #264,402, N = 500), we asked partisans (248 Democrats, 252 Republicans) to report their own attitudes on six environmental items (the GREEN consumption-values scale, 1–5,31) and to estimate how a typical outgroup member would respond to the same items. Partisans held large, asymmetric misperceptions of each other’s environmental attitudes (Figure 2A). Democrats sharply underestimated Republican attitudes toward environmental consumption (d = 1.49, SE = 0.10, p < .001)—an error large enough to misclassify the average Republican as opposed to green products rather than moderately supportive of them. Republicans were more calibrated about Democrats (d = 0.24, SE = 0.09, p = .01). The two misperceptions differ: the belief–reality gap is larger in the Democrat-judging-Republican direction than in the reverse (role × target interaction, t(996) = 11.5, p < .001). The bots themselves were imperfect guides: both held more extreme views than the partisans they represented. To recover the bots’ own attitudes, we presented each party-conditioned bot with the same six GREEN items and computed its position on each item as the expected response value, weighting the integers 1–5 by the model’s token probabilities at the first response position.29 The Republican-representing bot scored 3.084, below the Republican mean (3.728) by d = 0.61; the Democrat-representing bot scored 4.577, above the Democratic mean (4.149) by d = 0.56. Which direction the bot erred in mattered more than that it erred at all, because what helps a learner is a guide closer to the truth than their own starting beliefs.

Limitations. Most of the warmth effect fades within a week; a small residual concentrates among extreme partisans As expected for so brief an intervention, most of a single five-minute conversation’s effect faded within a week, though a small residual persisted among the most extreme partisans—the group these interventions most need to reach. The strength of synthetic contact is that it can be repeated: unlike a face-to-face meeting, a bot conversation is available again whenever and wherever partisans are already online. Even the immediate effect carries weight—right after the conversation, partisans were more likely than controls to choose a real outgroup exchange. Whether repeated synthetic contact can build lasting warmth is a question for future work. Two limitations qualify these findings. First, we indexed affective polarization mainly as outgroup warmth on a feeling thermometer—the standard measure, but one facet of a multidimensional construct33—and did not test social distance, trait attributions, or support for anti-democratic action. Second, the evidence comes from online U.S. partisans and two issues, environment and immigration, so generalization across populations and topics remains open. The belief-accuracy gains, observed in a within-person design, also warrant more caution than the experimentally isolated warmth effects.