United Nations practice shows that inclusivity is vital for mediation to be successful in helping end violent conflict and establish lasting peace. However, current methods for understanding the views and needs of populations during dynamic situations create tension between inclusivity and efficiency. This work introduces a novel paradigm to mitigate such tension. In partnership with collaborators at the United Nations we develop a realtime large-scale synchronous dialogue process (RLSDP) to understand stakeholder populations on an hour timescale. We demonstrate a machine learning model which enables each dialogue cycle to take place on a minute-timescale. We manage a key risk related to machine learning result trustworthiness by computing result confidence from a fast and reliable estimation of posterior variance. Lastly, we highlight a constellation of risks stemming from this new paradigm and suggest policies to mitigate them.

翻译：联合国的实践表明，包容性对于调解成功、帮助结束暴力冲突并建立持久和平至关重要。然而，在动态局势中理解人群观点和需求的现行方法在包容性与效率之间产生了矛盾。本文提出了一种缓解这一矛盾的新范式。我们与联合国合作方共同开发了实时大规模同步对话流程（RLSDP），能够在小时级时间尺度上理解利益相关方群体。我们展示了一种机器学习模型，使每个对话周期可在分钟级时间尺度内完成。通过基于快速可靠的方差后验估计计算结果置信度，我们管理了与机器学习结果可信度相关的关键风险。最后，我们重点阐述了这一新范式可能引发的一系列风险，并提出了缓解这些风险的政策建议。