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：在线人类对话量正快速增长。但在社交媒体平台、即时通讯应用及其他数字论坛的文本交互中，分歧与冲突可能滋生蔓延。这种有害言论加剧了两极分化，更重要的是，损害了多元社会为影响所有人的复杂社会问题制定有效解决方案的能力。学者与民间团体推广的干预措施虽能使线下人际对话减少分歧、提升效率，但将这些努力规模化应用于海量线上对话极具挑战性。我们通过一项大规模实验表明：人工智能工具可改善关于分歧话题的线上对话。具体而言，我们运用大语言模型提供基于证据的实时建议，旨在提升参与者被理解的感知程度。研究发现，这些干预措施能提升对话质量、降低政治分歧、改善语气，同时不会系统性改变对话内容或影响人们的政策立场。该发现对社交媒体研究、政治协商以及日益关注人工智能在计算社会科学中地位的学术群体具有重要启示。