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：暂无翻译