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A rapidly increasing amount of human conversation occurs online. Butdivisiveness and conflict can fester in text-based interactions on social mediaplatforms, in messaging apps, and on other digital forums. Such toxicityincreases polarization and, importantly, corrodes the capacity of diversesocieties to develop efficient solutions to complex social problems that impacteveryone. Scholars and civil society groups promote interventions that can makeinterpersonal conversations less divisive or more productive in offlinesettings, but scaling these efforts to the amount of discourse that occursonline is extremely challenging. We present results of a large-scale experimentthat demonstrates how online conversations about divisive topics can beimproved with artificial intelligence tools. Specifically, we employ a largelanguage model to make real-time, evidence-based recommendations intended toimprove participants' perception of feeling understood in conversations. Wefind that these interventions improve the reported quality of the conversation,reduce political divisiveness, and improve the tone, without systematicallychanging the content of the conversation or moving people's policy attitudes.These findings have important implications for future research on social media,political deliberation, and the growing community of scholars interested in theplace of artificial intelligence within computational social science.

AI Chat Assistants can Improve Conversations about Divisive Topics