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：在线人类对话的数量正迅速增长。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容易滋生分歧与冲突。这种有害言论加剧了两极分化，尤其削弱了多元社会针对影响所有人的复杂社会问题制定高效解决方案的能力。学者与公民社会团体倡导的干预措施在离线场景下能减少人际对话的分歧性、提升对话效率，但将这些措施推广至海量在线对话场景极具挑战性。我们展示了一项大规模实验的结果，证明人工智能工具能改善关于分歧性话题的在线对话质量。具体而言，我们采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被理解的感知度。研究发现，这些干预措施提升了对话的自报质量，降低了政治分歧程度，改善了对话语气，且未系统性改变对话内容或人们的政策立场。该发现对未来社交媒体研究、政治协商领域，以及日益壮大的关注人工智能在计算社会科学中作用的学者群体具有重要启示。