# Faster Peace via Inclusivity: An Efficient Paradigm to Understand Populations in Conflict Zones

arXiv: https://arxiv.org/abs/2311.00816 · HTML: https://mlsanity.com/paper/2311.00816/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [AI-Enhanced Deliberative Democracy and the Future of the Collective Will](https://mlsanity.com/paper/2503.05830/) (2025)
- [Toward Collective-Centric Evaluation of Preference Inference for Participatory Democracy](https://mlsanity.com/paper/2609.02990/) (2026)

## Abstract (arXiv)

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.