Using AI Persuasion to Reduce Political Polarization

Rising political polarization generates significant negative externalities for democratic institutions and economic stability, yet scalable interventions to reduce polarization remain scarce. In this paper, I study whether AI chatbots can reduce political polarization. In two preregistered online RCTs with representative U.S. samples, I find that AI significantly reduces polarization on the Ukraine war and immigration policy. In Experiment 1, AI reduced polarization by 20 percentage points, with effects persisting for one month. Experiment 2 pits AI against incentivized human persuaders and Static Text. I find no significant difference in effectiveness: all three reduced polarization by roughly 10 percentage points. While AI conversations were rated as more enjoyable, mechanism analysis reveals that persuasion is driven by learning and trust, not enjoyment. These results demonstrate AI’s scalable persuasive power, highlighting its dual‐use potential: it can be deployed to effectively reduce polarization, but also poses risks of misuse.

Walter, Johannes (2025), Using AI Persuasion to Reduce Political Polarization, ZEW Discussion Paper No. 25-071, Mannheim.