A rapidly increasing amount of human conversation occurs online. But divisiveness and conflict can fester in text-based interactions on social media platforms, in messaging apps, and on other digital forums. Such toxicity increases polarization and, importantly, corrodes the capacity of diverse societies to develop efficient solutions to complex social problems that impact everyone. Scholars and civil society groups promote interventions that can make interpersonal conversations less divisive or more productive in offline settings, but scaling these efforts to the amount of discourse that occurs online is extremely challenging. We present results of a large-scale experiment that demonstrates how online conversations about divisive topics can be improved with artificial intelligence tools. Specifically, we employ a large language model to make real-time, evidence-based recommendations intended to improve participants' perception of feeling understood in conversations. We find that these interventions improve the reported quality of the conversation, reduce political divisiveness, and improve the tone, without systematically changing 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 the place of artificial intelligence within computational social science.

翻译：在线人类对话数量正快速增长。然而，社交媒体平台、即时通讯应用及其他数字论坛中的文本交互往往滋生分裂与冲突。这种毒性言论加剧了两极分化，更重要的是，侵蚀了多元社会为解决影响所有人的复杂社会问题而制定有效解决方案的能力。学者与公民社会团体推动的干预措施虽能在线下环境中降低人际对话的分裂性、提升建设性，但要将这些努力扩展到海量的在线对话中极具挑战。我们展示了一项大规模实验的结果，证明人工智能工具能够改善关于分歧性话题的在线对话质量。具体而言，我们利用大型语言模型提供基于证据的实时建议，旨在提升参与者在对话中感受到被理解的体验。研究发现，这些干预措施能改善对话的感知质量、降低政治分裂性并优化交流语气，同时不会系统性改变对话内容或动摇人们的政策立场。这些发现对社交媒体研究、政治审议以及日益壮大的关注人工智能在计算社会科学中地位的学者群体具有重要启示意义。