Using collective dialogues and AI to find common ground between Israeli and Palestinian peacebuilders
Name
3715275.3732022.pdf
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2 MB
Format
Adobe PDF
Checksum (MD5)
637d72eaaceb79d23f090bb5edddd9a1
Author(s)
Konya, Andrew
Thorburn, Luke
Almasri, Wasim
Leshem, Oded Adomi
Procaccia, Ariel
Schirch, Lisa
Bakker, Michiel
Date Issued
June 23, 2025
Publisher
ACM|The 2025 ACM Conference on Fairness, Accountability, and Transparency
Citation
Andrew Konya, Luke Thorburn, Wasim Almasri, Oded Adomi Leshem, Ariel Procaccia, Lisa Schirch, and Michiel Bakker. 2025. Using collective dialogues and AI to find common ground between Israeli and Palestinian peacebuilders. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT '25). Association for Computing Machinery, New York, NY, USA, 312–333.
Version
Final published version
Abstract
A growing body of work has shown that AI-assisted methods — leveraging large language models, social choice methods, and collective dialogues — can help navigate polarization and surface common ground in controlled lab settings. But what can these approaches contribute in real-world contexts? We present a case study applying these techniques to find common ground between Israeli and Palestinian peacebuilders in the period following October 7th, 2023. From April to July 2024 an iterative deliberative process combining LLMs, bridging-based ranking, and collective dialogues was conducted in partnership with the Alliance for Middle East Peace. Around 138 civil society peacebuilders participated including Israeli Jews, Palestinian citizens of Israel, and Palestinians from the West Bank and Gaza. The process resulted in a set of collective statements, including demands to world leaders, with at least 84% agreement from participants on each side. In this paper, we document the process, results, challenges, and important open questions.
Description
FAccT ’25, Athens, Greece
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3715275.3732022
