Artificial Intelligence for Conflict Early Warning: A Comparative Analysis of AI-Enabled Forecasting Systems and Implications for Peacebuilding Policy

Abstract

Artificial Intelligence (AI) is transforming how governments, international organizations, and peacebuilding actors anticipate violent conflict. Conflict early-warning systems now integrate machine learning, natural-language processing, and geospatial analysis to forecast political violence with increasing sophistication. Yet the proliferation of such systems raises a policy-relevant question that has received insufficient systematic attention: how do existing AI-enabled early-warning systems actually compare in scope, method, transparency, and demonstrated accuracy, and what do these differences imply for how policymakers should use their outputs? This article addresses that gap through a systematic comparative analysis of five prominent AI-enabled conflict early-warning systems - the Violence & Impacts Early-Warning System (ViEWS), the Global Conflict Risk Index (GCRI), the Early Warning Project (EWP), PREVIEW, and Conflict Forecast - evaluated across geographic scope, forecasting method, update frequency, transparency, and reported predictive performance. Drawing on this comparison and on the broader literature on conflict forecasting and preventive peacebuilding, the article develops a Human-Centred AI Peacebuilding Framework that positions AI as a decision-support mechanism rather than an autonomous predictor of, or solution to, violent conflict. The analysis finds substantial variation across systems in transparency and accessibility, meaningful overlap in the countries they flag as high-risk, and a persistent gap between predictive sophistication and institutional capacity to act on warnings. The article concludes with policy recommendations for governments and peacebuilding institutions - including those in South Asia, a region that recurs among the highest-risk countries in several of the reviewed systems - on the responsible integration of AI-enabled early warning into conflict-prevention practice.