  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.
